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We propose a method to improve search time and space complexity in statistical machine translation architectures, by employing linguistic bracketing information on the source language sentence. It is one of …...

Language modeling is to associate a sequence of words with a priori probability, which is a key part of many natural language applications such as speech recognition and statisticM lna- …...

In statistical natural language process- ing we always face the problem of sparse data. One way to reduce this problem is to group words into equivalence classes which is a …...

Extractive summarization techniques cannot generate document summaries shorter than a single sentence, some- thing that is often required. An ideal summarization system would under- stand each document and generate an …...

Aligned parallel corpora have proved very useful in many natural language processing tasks, including statistical machine translation and word sense disambiguation. In this paper, we describe an alignment technique for …...

AbstractIn the framework of statistical machine translation (SMT), correspondences between the words in the source and the target language are learned from bilingual corpora on the basis of so-called alignment …...

This paper gives an overview of our work on statistical ma- chine translation of spoken dialogues, in particular in the framework of the Verbmobil project. The goal of the Verbmobil …...

We present a framework for statistical machine translation of natural languages based on direct maximum entropy mod- els, which contains the widely used sour- ce-channel approach as a special case. …...

There has been much interest in us- ing phrasal movement to improve statis- tical machine translation. We explore how well phrases cohere across two lan- guages, specifically English and French, …...

This paper describes an alternative trans- lation model based on a text chunk un- der the framework of statistical machine translation. The translation model sug- gested here first performs chunking. …...

The purpose of this work is to investigate the use of machine learning approaches for confi- dence estimation within a statistical machine translation application. Specifically, we at- tempt to learn …...

We define noun phrase translation as a subtask of machine translation. This en- ables us to build a dedicated noun phrase translation subsystem that improves over the currently best general …...

Language model based IR system proposed in recent 5 years has introduced the language model approach in the speech recognition area into the IR community and improves the performance of …...

Based upon a statistically trained speech translation system, in this study, we try to combine distinctive features derived from the two modules: speech recognition and statistical machine translation, in a …...

This paper describes a project to detect dependen- cies between Japanese phrasal units called bunsetsus, and sentence boundaries in a spontaneous speech corpus. In monologues, the biggest problem with de- …...

This paper presents example-based machine translation (MT) based on syntactic trans- fer, which selects the best translation by us- ing models of statistical machine translation. Example-based MT sometimes generates in- …...

In statistical machine translation, the currently best performing systems are based in some way on phrases or word groups. We describe the baseline phrase-based translation system and various refinements. We …...

Texts from the medical domain are an important task for natural language processing. This paper investigates the usefulness of a large medical database (the Unified Medical Language System) for the …...

. In this paper we introduce Linear B's statistical machine translation system. We describe how Linear B's phrase-based translation models are learned from a parallel corpus, and show how the …...

We explore unsupervised language model adaptation techniques for Statistical Machine Translation. The hypotheses from the machine translation output are converted into queries at different levels of representation power and used …...

We present Minimum Bayes-Risk (MBR) de- coding for statistical machine translation. This statistical approach aims to minimize expected loss of translation errors under loss functions that measure translation performance. We …...

We apply statistical machine translation (SMT) tools to generate novel paraphrases of input sentences in the same language. The system is trained on large volumes of sentence pairs automatically extracted …...

In statistical machine translation, the gen- eration of a translation hypothesis is com- putationally expensive. If arbitrary re- orderings are permitted, the search prob- lem is NP-hard. On the other …...

In an ordinary syntactic parser, the input is a string, and the grammar ranges over strings. This paper explores generalizations of ordinary parsing algo- rithms that allow the input to …...

. In this paper, we present a translation memory (TM) based system to augment a statistical translation (SMT) system. It is used for translating sentences which have close matches in …...

Existing named entity (NE) transliteration approaches often exploit a general model to transliterate NEs, regardless of their origins. As a result, both a Chinese name and a French name (assuming …...

. Fully automatic translations are far from being perfect. Non-grammatical sentences are often produced by automatic systems and there is even no guarantee that the meaning of the sentence is …...

. Discriminative training methods are used in statistical machine translation to ef- fectively introduce and combine additional knowledge sources within the translation proc- ess. Although these methods are described in …...

We describe a novel approach to statistical machine translation that combines syntactic information in the source language with recent advances in phrasal translation. This method requires a source-language dependency parser, …...

. In this paper, we present an empirical study that utilizes morph-syntactical information to improve translation quality. With three kinds of language pairs matched according to morph-syntactical similar- ity or …...

In statistical machine translation, estimat- ing word-to-word alignment probabilities for the translation model can be difficult due to the problem of sparse data: most words in a given corpus occur …...

A Chinese sentence is represented as a sequence of charac- ters, and words are not separated from each other. In statisti- cal machine translation, the conventional approach is to seg- …...

ISI entered two statistical machine translation systems in the IWSLT evaluation this year: one was phrase-based and the other syntax-based. The syntax-based system represents the results of a current research …...

Statistical machine translation relies heavily on the available training data. In some cases it is necessary to limit the amount of training data that can be created for or actually …...

. In statistical machine translation, word alignment models are trained on bilingual corpora. Long sentences pose severe problems: 1. the high computational requirements; 2. the poor quality of the resulting …...

We present a method for improving statistical machine translation perfor- mance by using linguistically motivated syntactic information. Our algo- rithm recursively decomposes source language sentences into syntactically simpler and shorter …...

AbstractStatistical Machine Translation (SMT) accuracy degrades when there is only a limited amount of training, or when the training is not from the same domain or genre of text as …...

Statistical machine translation is quite ro- bust when it comes to the choice of in- put representation. It only requires con- sistency between training and testing. As a result, there …...

In this paper we study a set of prob- lems that are of considerable importance to Statistical Machine Translation (SMT) but which have not been addressed satis- factorily by the …...

This paper presents a pilot study of the use of phrasal Statistical Machine Trans- lation (SMT) techniques to identify and correct writing errors made by learners of English as a …...

This paper investigates the use of morpho- syntactic information to reduce data- sparseness in statistical machine transla- tion from Spanish to English. In particular, word-alignment training is performed by applying …...

In this paper we propose a generalization of the Stack-based decoding paradigm for Statistical Machine Translation. The well known single and multi-stack decoding algorithms defined in the literature have been …...

We present an approach to statistical machine translation that combines ideas from phrase-based SMT and traditional grammar-based MT. Our system incor- porates the concept of multi-word trans- lation units into …...

For many years, statistical machine trans- lation relied on generative models to pro- vide bilingual word alignments. In 2005, several independent efforts showed that discriminative models could be used to …...

AbstractThis paper presents our study of exploiting morpho-syntactic information for phrase-based statistical machine translation (SMT). For morphological transformation, we use hand-crafted transformational rules. For syntactic transformation, we propose a transformational …...

AbstractThis paper presents a reordering framework for statistical machine translation (SMT) where source-side reorderings are integrated into SMT decoding, allowing for a highly constrained reordered search graph. The monotone search …...

Complex Language Models cannot be eas- ily integrated in the first pass decoding of a Statistical Machine Translation system – the decoder queries the LM a very large number of …...

We present a novel statistical approach to semantic parsing, WASP, for construct- ing a complete, formal meaning represen- tation of a sentence. A semantic parser is learned given a set …...

We propose a novel reordering model for phrase-based statistical machine transla- tion (SMT) that uses a maximum entropy (MaxEnt) model to predicate reorderings of neighbor blocks (phrase pairs). The model …...

Mixture modelling is a standard pattern classification technique. However, in statistical machine translation, the use of mixture modelling is still un- explored. Two main advantages of the mixture approach are …...

This work presents translation results for the three data sets made available in the shared task “Exploiting Parallel Texts for Statistical Machine Translation” of the HLT-NAACL 2006 Workshop on Statisti- …...

In statistical machine translation, large numbers of parallel sentences are required to train the model parameters. However, plenty of the bilingual language resources available on web are aligned only at …...

Statistical MT has made great progress in the last few years, but current translation models are weak on re-ordering and target language fluency. Syn- tactic approaches seek to remedy these …...

Systems based on synchronous grammars and tree transducers promise to improve the quality of statistical machine transla- tion output, but are often very computa- tionally intensive. The complexity is ex- …...

We present translation results on the shared task ”Exploiting Parallel Texts for Statistical Machine Translation” gener- ated by a chart parsing decoder operating on phrase tables augmented and general- ized …...

We investigate the impact of parse quality on a syntactically-informed statistical ma- chine translation system applied to techni- cal text. We vary parse quality by vary- ing the amount of …...

AbstractAccurately translating multiword expressions is important to obtain good performance in machine translation, cross-language information retrieval, and other multilingual tasks in human language technology. Existing approaches to inducing translation equivalents …...

We propose a novel HMM-based framework to accurately transliterate unseen named entities. The framework leverages features in letter- alignment and letter n-gram pairs learned from available bilingual dictionaries. Letter-classes, such …...

We describe a discriminatively trained se- quence alignment model based on the av- eraged perceptron. In common with other approaches to sequence modeling using per- ceptrons, and in contrast with …...

We address the problem of training the free parameters of a statistical machine transla- tion system. We show significant improve- ments over a state-of-the-art minimum er- ror rate training baseline …...

One main challenge of statistical machine trans- lation (SMT) is dealing with word order. The main idea of the statistical machine reordering (SMR) approach is to use the powerful tech- …...

We propose a domain specific model for statistical machine translation. It is well- known that domain specific language mod- els perform well in automatic speech recog- nition. We show that …...

Current statistical machine translation sys- tems handle the translation process as the transformation of a string of symbols into another string of symbols. Normally the symbols dealt with are the …...

Syntactic reordering approaches are an ef- fective method for handling word-order dif- ferences between source and target lan- guages in statistical machine translation (SMT) systems. This paper introduces a re- …...

Mixture modelling is a standard technique for density estimation, but its use in sta- tistical machine translation (SMT) has just started to be explored. One of the main advantages of …...

Statistical machine translation, as well as other areas of human language processing, have recently pushed toward the use of large scale n-gram language models. This paper presents efficient algorithmic and …...

This paper explores the use of statisti- cal machine translation (SMT) methods for tactical natural language generation. We present results on using phrase-based SMT for learning to map meaning repre- …...

A major engineering challenge in statistical machine translation systems is the efficient representation of extremely large translation rulesets. In phrase-based models, this prob- lem can be addressed by storing the …...

Parallel corpus is an indispensable resource for translation model training in statistical machine translation (SMT). Instead of col- lecting more and more parallel training corpora, this paper aims to improve …...

We describe an approach to improve Statistical Machine Translation (SMT) performance using multi-lingual, parallel, sentence-aligned corpora in several bridge languages. Our approach consists of a sim- ple method for utilizing …...

We describe an architecture that allows to combine statistical machine translation (SMT) with rule-based machine translation (RBMT) in a multi-engine setup. We use a variant of standard SMT technology to …...

AbstractIn this paper, we describe our machine translation system which was used for the Chinese-to-English task in the IWSLT2007 evaluation campaign. The system is a statistical machine translation (SMT) system, …...

We achieved a state of the art performance in statistical machine translation by using a large number of features with an online large-margin training algorithm. The mil- lions of parameters …...

This paper presents a Function Word cen- tered, Syntax-based (FWS) solution to ad- dress phrase ordering in the context of statistical machine translation (SMT). Mo- tivated by the observation that …...

Reordering model is important for the sta- tistical machine translation (SMT). Current phrase-based SMT technologies are good at capturing local reordering but not global reordering. This paper introduces syntactic knowledge …...

We present an approach to query expan- sion in answer retrieval that uses Statisti- cal Machine Translation (SMT) techniques to bridge the lexical gap between ques- tions and answers. SMT-based …...

This paper presents a new paradigm for translation from inflectionally rich lan- guages that was used in the University of Maryland statistical machine transla- tion system for the WMT07 Shared …...

AbstractWe present the University of Edinburgh’s submission for the IWSLT 2007 shared task. Our efforts focused on adapting our statistical machine translation system to the open data conditions for the …...

Statistical machine translation systems are usually trained on large amounts of bilin- gual text and monolingual text in the tar- get language. In this paper we explore the use of …...

Today's statistical machine translation systems generalize poorly to new domains. Even small shifts can cause precipitous drops in translation quality. Phrasal systems rely heavily, for both reordering and contextual translation, …...

AbstractIn this paper we describe the statistical machine translation system developed at ITI/UPV, which aims especially at speech recognition and statistical machine translation integration, for the evaluation campaign of the …...

We introduce a new method for automatically disambiguation of word translations by using dependency relationships. In our approach, we learn the relationships between translations and dependency relationships from a parallel …...

. Log-linear models represent nowadays the state-of-the-art in statistical machine translation. There, several models are combined al- together into a whole statistical approach. Finite-state transducers cons- titute a special type …...

. Boosting is a general method for improving the accuracy of a given learning algorithm under certain restrictions. In this work, AdaBoost, one of the most popular boosting algorithms, is …...

We improve the quality of statistical machine translation (SMT) by applying models that predict word forms from their stems using extensive morphological and syntactic infor- mation from both the source …...

This paper proposes a novel method that ex- ploits multiple resources to improve statisti- cal machine translation (SMT) based para- phrasing. In detail, a phrasal paraphrase ta- ble and a …...

tra Statistical machine translation systems are usually trained on large amounts of bilingual text and monolingual text. In this paper, we propose a method to per- form domain adaptation for …...

. This paper focuses on domain specific use of MT with a special focus on SMT in the workflow of a Language Service Provider (LSP). We report on the feedback …...

This paper presents a technique for class- dependent decoding for statistical machine translation (SMT). The approach differs from previous methods of class-dependent transla- tion in that the class-dependent forms of …...

Latent Semantic Analysis (LSA) is based on the Singular Value Decompo- sition (SVD) of a term-by-document matrix for identifying relationships among terms and documents from co- occurrence patterns. Among the …...

. Recent research has shown the importance of using source context information to disambiguate source phrases in phrase-based Sta- tistical Machine Translation. Although encouraging results have been obtained, those studies …...

. This paper presents two hybrid architectures combining rule- based and statistical machine translation (RBMT and SMT) technology. In the first case, several existing MT engines are combined in a …...

Chinese word segmentation (CWS) is a necessary step in Chinese-English statisti- cal machine translation (SMT) and its per- formance has an impact on the results of SMT. However, there are …...

This paper proposes a novel lexicalized ap- proach for rule selection for syntax-based statistical machine translation (SMT). We build maximum entropy (MaxEnt) mod- els which combine rich context informa- tion …...

Traditionally, statistical machine translation systems have relied on parallel bi-lingual data to train a translation model. While bi-lingual parallel data are expensive to generate, mono- lingual data are relatively common. …...

Minimum Error Rate Training (MERT) is an effective means to estimate the feature func- tion weights of a linear model such that an automated evaluation criterion for measuring system performance …...

Bracketing Transduction Grammar (BTG) is a natural choice for effective integration of desired linguistic knowledge into sta- tistical machine translation (SMT). In this paper, we propose a Linguistically Anno- tated …...

Measure words in Chinese are used to indi- cate the count of nouns. Conventional sta- tistical machine translation (SMT) systems do not perform well on measure word generation due to …...

We explore the augmentation of statistical ma- chine translation models with features of the context of each phrase to be translated. This work extends several existing threads of re- search …...

Many reordering approaches have been proposed for the statistical machine translation (SMT) system. However, the information about the type of source sentence is ignored in the previous works. In this …...

The Edinburgh submissions to the shared task of the Third Workshop on Statistical Machine Translation (WMT-2008) incorporate recent advances to the open source Moses system. We made a special effort …...

Based on an architecture that allows to com- bine statistical machine translation (SMT) with rule-based machine translation (RBMT) in a multi-engine setup, we present new results that show that this …...

In this paper we describe a new approach to model long-range word reorderings in statistical machine translation (SMT). Un- til now, most SMT approaches are only able to model local …...

Reordering is a serious challenge in sta- tistical machine translation. We propose a method for analysing syntactic reorder- ing in parallel corpora and apply it to un- derstanding the differences …...

We present a series of empirical studies aimed at illuminating more precisely the likely contribution of semantic roles in im- proving statistical machine translation ac- curacy. The experiments reported study …...

Current statistical machine translation (SMT) systems are trained on sentence- aligned and word-aligned parallel text col- lected from various sources. Translation model parameters are estimated from the word alignments, and …...

Current phrase-based statistical machine translation systems process each test sentence in isolation and do not enforce global consis- tency constraints, even though the test data is often internally consistent with …...

Untranslated words still constitute a ma- jor problem for Statistical Machine Trans- lation (SMT), and current SMT systems are limited by the quantity of parallel training texts. Augmenting the training …...

In this paper, a human evaluation of a Catalan-Spanish Ngram-based statistical machine translation system is used to de- velop specific techniques based on the use of grammatical categories, lexical cate- …...

The BLEU scores and translation fluency for the current state-of-the-art SMT systems based on IBM models are still too low for publication purposes. The major issue is that stochastically generated …...

State-of-the-art Machine Translation (MT) sys- tems are still far from being perfect. An alterna- tive is the so-called Interactive Machine Transla- tion (IMT) framework. In this framework, the knowledge of …...

This paper describes our Statistical Ma- chine Translation systems for the WMT09 (en:fr) shared task. For this evaluation, we have developed four systems, using two different MT Toolkits: our primary …...

Recent advances in statistical machine translation have used beam search for approximate NP-complete inference within probabilistic translation models. We present an alternative approach of sampling from the posterior distribution defined …...

We present a simple and effective method for extracting parallel sentences from comparable corpora. We employ a sta- tistical machine translation (SMT) system built from small amounts of parallel texts …...

Segmentation is widely used in adapting Statistical Machine Translation to highly inflected languages as Basque. The way this segmentation is carried out impacts on the quality of the translation. In …...

RWTH participated in the shared transla- tion task of the Fourth Workshop of Sta- tistical Machine Translation (WMT 2009) with the German-English, French-English and Spanish-English pair in each transla- tion …...

This study presents the TALP-UPC sub- mission to the EACL Fourth Worskhop on Statistical Machine Translation 2009 evaluation campaign. It outlines the ar- chitecture and configuration of the 2009 phrase-based …...

We introduce a novel precedence reordering approach based on a dependency parser to sta- tistical machine translation systems. Similar to other preprocessing reordering approaches, our method can efficiently incorporate linguis- …...

Current statistical machine translation sys- tems usually extract rules from bilingual corpora annotated with 1-best alignments. They are prone to learn noisy rules due to alignment mistakes. We propose a …...

This paper presents a direct word reordering model with novel syntax-based features for sta- tistical machine translation. Reordering models address the problem of reordering source lan- guage into the word …...

Phrase reordering is of great importance for statistical machine translation. Ac- cording to the movement of phrase trans- lation, the pattern of phrase reordering can be divided into three classes: …...

This paper introduces a new statistical pre- processing model for Rule-Based Machine Translation (RBMT) systems. We train a Statistical Machine Translation (SMT) system using monolingual corpora. This model can transform …...

AbstractWe examine pooling data as a method for improving Statistical Machine Translation (SMT) quality for narrowly defined domains, such as data for a particular company or public entity. By pooling …...

AbstractLarge scale parallel data generation for new language pairs requires intensive human effort and availability of experts. It becomes immensely difficult and costly to provide Statistical Machine Translation (SMT) systems …...

This paper describes a statistical machine translation system for our participation for the WMT10 shared task. Based on MOSES, our system is capable of translat- ing German, French and Spanish …...

This paper describes the Aalto submission for the German-to-English and the Czech- to-English translation tasks of the ACL 2010 Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR. Statistical machine …...

This paper proposes new algorithms to com- pute the sense similarity between two units (words, phrases, rules, etc.) from parallel cor- pora. The sense similarity scores are computed by using …...

We propose a translation recommendation framework to integrate Statistical Machine Translation (SMT) output with Transla- tion Memory (TM) systems. The frame- work recommends SMT outputs to a TM user when …...

In this paper we investigate the challenges of applying statistical machine translation to meeting conversations, with a particu- lar view towards analyzing the importance of modeling contextual factors such as …...

AbstractStatistical machine translation to morphologically richer languages is a challenging task and more so if the source and target languages differ in word order. Current state-of-the-art MT systems thus deliver …...

We present a method for incorporat- ing arbitrary context-informed word at- tributes into statistical machine trans- lation by clustering attribute-quali ed source words, and smoothing their word translation probabilities using …...

We propose a structure called dependency forest for statistical machine translation. A dependency forest compactly represents multiple dependency trees. We develop new algorithms for extracting string-to- dependency rules and training …...

AbstractThis paper deals with translation of English documents to Oromo using statistical methods. Whereas English is the lingua franca of online information, Oromo, despite its relative wide distribution within Ethiopia …...

Machine Translation (MT) is the task of automatically translating a text from one language to another. In this work we de- scribe a phrase-based Statistical Machine Translation (SMT) system that …...

AbstractThe paper presents an approach for constructing a weighted bilingual dictionary of inflectional forms using as input data a traditional bilingual dictionary, and not parallel corpora. An algorithm is developed …...

AbstractWe present the methodology that underlies mew metrics for semantic machine translation evaluation we are developing. Unlike widely-used lexical and n-gram based MT evaluation metrics, the aim of semantic MT …...

A novel and robust approach to improv- ing statistical machine translation fluency is developed within a minimum Bayes- risk decoding framework. By segment- ing translation lattices according to con- fidence …...

We describe the effort of the Microsoft Translator team to develop a Haitian Cre- ole statistical machine translation engine from scratch in a matter of days. Haitian Creole presents a …...

English is a typical SVO (Subject-Verb- Object) language, while Japanese is a typ- ical SOV language. Conventional Statis- tical Machine Translation (SMT) systems work well within each of these language …...

We present new results from a hybrid com- bination of rule-based machine translation (RBMT) with a variant of statistical ma- chine translation (SMT) that supports hier- archical structures and is …...

Compound splitting is an important prob- lem in many NLP applications which must be solved in order to address issues of data sparsity. Previous work has shown that lin- guistic …...

 This paper proposes to use monolingual collocations to improve Statistical Ma- chine Translation (SMT). We make use of the collocation probabilities, which are estimated from monolingual corpora, in two …...

This paper proposes an unsupervised word segmentation algorithm that identi- fies word boundaries in continuous source language text in order to improve the translation quality of statistical machine translation (SMT) …...

We reveal a previously unnoticed connection between dependency parsing and statistical machine translation (SMT), by formulating the dependency parsing task as a problem of word alignment. Furthermore, we show that …...

Statistical machine translation relies heav- ily on parallel corpora to train its mod- els for translation tasks. While more and more bilingual corpora are readily avail- able, the quality of …...

We present a general method for incorporating an “expert” model into a Statistical Machine Transla- tion (SMT) system, in order to improve its perfor- mance on a particular “area of …...

This paper describes our Statistical Ma- chine Translation systems for the WMT10 evaluation, where LIMSI participated for two language pairs (French-English and German-English, in both directions). For German-English, we concentrated …...

AbstractIn this paper we use statistical machine translation and morphology information from two different morphological analyzers to try to improve translation quality by linguistically motivated segmentation. The morphological analyzers we …...

We present Mixture Model-based Mini- mum Bayes Risk (MMMBR) decoding, an approach that makes use of multiple SMT systems to improve translation ac- curacy. Unlike existing MBR decoding methods defined …...

The problem of language model adaptation in statistical machine translation is consid- ered. A mixture of language models is employed, which is obtained by clustering the bilingual training data. Unsupervised …...

Weights of the various components in a standard Statistical Machine Translation model are usually estimated via Minimum Error Rate Training. With this, one finds their optimum value on a development …...

AbstractCurrently, a great effort is being carried out in the digitalisation of large historical document collections for preservation purposes. The documents in these collections are usually written in ancient languages, …...

This paper investigates the impact of mis- spelled words in statistical machine transla- tion and proposes an extension of the transla- tion engine for handling misspellings. The en- hanced system …...

We conduct a pilot study for task-oriented evaluation of Multiword Expression (MWE) in Statistical Machine Translation (SMT). We propose two different integration strategies for MWE in SMT, which take advantage …...

This paper describes our phrase-based Sta- tistical Machine Translation (SMT) sys- tem for the WMT10 Translation Task. We submitted translations for the German to English and English to German transla- …...

In this paper we describe the statisti- cal machine translation system of the RWTH Aachen University developed for the translation task of the Fifth Workshop on Statistical Machine Translation. State- …...

AbstractThe SignSpeak project will be the first step to approach sign language recognition and translation at a scientific level already reached in similar research fields such as automatic speech recognition …...

The translation model of statistical ma- chine translation systems is trained on par- allel data coming from various sources and domains. These corpora are usually con- catenated, word alignments are …...

Previous methods on improving transla- tion quality by employing multiple SMT models usually carry out as a second- pass decision procedure on hypotheses from multiple systems using extra fea- tures …...

AbstractOne problem in statistical machine translation (SMT) is that the output often is ungrammatical. To address this issue, we have investigated the use of a grammar checker for two purposes …...

. In statistical machine translation, the number of sentence pairs in the bilingual corpus is very important to the quality of translation. However, when the quantity reaches some extent, enlarging …...

Preordering of source side sentences has proved to be useful in improving statistical machine translation. Most work has used a parser in the source language along with rules to map …...

In this paper we study several advanced techniques and models for Arabic-to- English statistical machine translation. We examine how the challenges imposed by this particular language pair and transla- tion …...

Languages with rich inflectional morphology pose a difficult challenge for statistical ma- chine translation. To address the problem of morphologically inconsistent output, we add unification-based constraints to the target-side of …...

Keyphrase extraction aims to select a set of terms from a document as a short summary of the document. Most methods extract keyphrases according to their statistical prop- erties in …...

Statistical machine translation systems have greatly improved in the last years. However, this boost in performance usu- ally comes at a high computational cost, yielding systems that are often not …...

This paper describes efforts towards the development of an Arabic to Italian SMT system for the news domain. Since only very little parallel data are available for this language pair, …...

This paper describes a method to effi- ciently leverage Brazilian Portuguese re- sources as European Portuguese resources. Brazilian Portuguese and European Por- tuguese are two Portuguese varieties very close and …...

As developers of a highly multilingual named entity recognition (NER) system, we face an evaluation resource bottleneck problem: we need evaluation data in many languages, the annotation should not be …...

Statistical machine translation systems are usually trained on a large amount of bilingual sentence pairs and translate one sentence at a time, ignoring document-level information. In this paper, we propose …...

This paper describes the statistical machine translation system submitted to the WMT11 Featured Translation Task, which involves translating Haitian Creole SMS messages into English. In our experiments we try to …...

This paper presents a study conducted in the course of implementing a project in the World Intellectual Property Organiza- tion (WIPO) on assisted translation of pat- ent abstracts and titles …...

We present a discriminative learning method to improve the consistency of translations in phrase-based Statistical Machine Translation (SMT) systems. Our method is inspired by Translation Memory (TM) systems which are …...

We present a data-driven approach to generat- ing responses to Twitter status posts, based on phrase-based Statistical Machine Translation. We find that mapping conversational stimuli onto responses is more difficult …...

We present a novel strategy to derive new translation units using an additional bilin- gual corpus and a previously trained SMT system. The units were used to adapt the SMT …...

We explore efficient domain adaptation for the task of statistical machine translation based on extracting sentences from a large general- domain parallel corpus that are most relevant to the target …...

Paraphrases are useful for statistical machine translation (SMT) and natural language pro- cessing tasks. Distributional paraphrase gen- eration is independent of parallel texts and syntactic parses, and hence is suitable …...

This paper presents a study conducted in the course of implementing a project in the World Intellectual Property Organiza- tion (WIPO) on assisted translation of pat- ent abstracts and titles …...

State-of-the-art statistical machine translation (MT) systems have made significant progress towards producing user-acceptable translation output. However, there is still no efficient way for MT systems to inform users which words …...

We use target-side monolingual data to ex- tend the vocabulary of the translation model in statistical machine translation. This method called “reverse self-training” improves the de- coder’s ability to produce …...

. In this paper, we investigate the language model (LM) adaptation issue for Statis- tical Machine Translation (SMT). In order to overcome the weight bias on the LM obtained from …...

This paper describes LIMSI’s submissions to the Sixth Workshop on Statistical Machine Translation. We report results for the French- English and German-English shared transla- tion tasks in both directions. Our …...

This paper describes the development of French–English and English–French statisti- cal machine translation systems for the 2011 WMT shared task evaluation. Our main sys- tems were standard phrase-based statistical systems …...

Unsupervised word alignment is most often modeled as a Markov process that generates a sentence f conditioned on its translation e. A similar model generating e from f will make …...

A Statistical Machine Translation (SMT) system generates an n-best list of candidate translations for each sentence. A model er- ror occurs if the most probable translation (1-best) generated by the …...

Event extraction systems typically take ad- vantage of language and domain-specific knowledge bases, including patterns that are used to identify specific facts in text; techniques to acquire these patterns can …...

Most statistical machine translation systems rely on composed rules (rules that can be formed out of smaller rules in the grammar). Though this practice improves translation by weakening independence assumptions …...

We propose a method to improve the accuracy of parsing bilingual texts (bitexts) with the help of statistical machine translation (SMT) systems. Previous bitext parsing methods use human-annotated bilingual treebanks …...

Part-of-speech language modeling is com- monly used as a component in statistical ma- chine translation systems, but there is mixed evidence that its usage leads to significant im- provements. We …...

This paper reports on the ongoing work fo- cused on domain adaptation of statistical machine translation using domain-specific data obtained by domain-focused web crawling. We present a strategy for crawl- …...

We propose a simple training regime that can improve the extrinsic performance of a parser, given only a corpus of sentences and a way to automatically evaluate the extrinsic quality …...

Current statistical machine translation (SMT) systems are stated to be dependent on the availability of a very large training data for producing the language and trans- lation models. Unfortunately, large …...

AbstractAchieving accurate translation, especially in multiple domain documents with statistical machine translation systems, requires more and more bilingual texts and this need becomes more critical when training such systems for …...

Integration of domain specific knowledge into a general purpose statistical machine translation (SMT) system poses challenges due to insufficient bilingual corpora. In this paper we propose a simplification-translation-restoration (STR) framework …...

Statistical post-editing (SPE) of the output produced by rule-based MT (RBMT) systems has been reported to produce extraordinary BLEU (and other automatic evaluation) score improvements. SPE has also been applied …...

This paper presents a proposed method integrated with three statistical models including Translation model, Query generation model and Document retrieval model for cross-language document retrieval. Given a certain document in …...

AbstractArabic segmentation was already applied successfully for the task of statistical machine translation (SMT). Yet, there is no consistent comparison of the effect of different techniques and methods over the …...

AbstractRetrieving research papers and patents is important for any researcher assessing the scope of a field with high industrial relevance. However, the terms used in patents are often more abstract …...

AbstractThis paper describes the development of a statistical machine translation system between French and English for scientific papers. This system will be closely integrated into the French HAL open archive, …...

Many effective adaptation techniques for statistical machine translation crucially rely on in-domain development sets to learn model parameters. In this paper we present a novel method that automatically generates the …...

Unlike most Western languages, there are no typographic boundaries between words in written Japanese and Chinese. Word seg- mentation is thus normally adopted as an ini- tial step in most …...

AbstractWikipedia articles in different languages have been mined to support various tasks, such as Cross-Language Information Retrieval (CLIR) and Statistical Machine Translation (SMT). Articles on the same topic in different …...

In this paper, we address statistical ma- chine translation of public conference talks. Modeling the style of this genre can be very challenging given the shortage of available in-domain training …...

AbstractIn Statistical Machine Translation, words that were not seen during training are unknown words, that is, words that the system will not know how to translate. In this paper we …...

We compare three methods of modeling morphological features in statistical ma- chine translation (SMT) from English to Arabic, a morphologically rich language. Features can be modeled as part of the …...

Nowadays, there are large amounts of data available to train statistical machine trans- lation systems. However, it is not clear whether all the training data actually help or not. A …...

We tackle the problem of domain adapta- tion of Statistical Machine Translation by exploiting domain-specific data acquired by domain-focused web-crawling. We de- sign and evaluate a procedure for auto- matic …...

AbstractWe discuss a previously proposed method for augmenting parallel corpora of limited size for the purposes of machine translation through monolingual paraphrasing of the source language. We develop a three-stage …...

A forced derivation tree (FDT) of a sentence pair {f, e} denotes a derivation tree that can translate f into its accurate target translation e. In this paper, we present …...

The Web is an ever increasing, dynamically changing, multilingual repository of text. There have been several approaches to harvest this repository for bootstrapping, supplementing and adapting data needed for training …...

We describe the Heidelberg University system for the Cross-lingual Textual Entailment task at SemEval-2012. The system relies on fea- tures extracted with statistical machine trans- lation methods and tools, combining …...

This paper presents a range of preprocess- ing solutions for Hebrew-English statisti- cal machine translation. Our best system, using a morphological analyzer, increases 3.5 BLEU points over a no-tokenization baseline …...

We present Joshua 4.0, the newest version of our open-source decoder for parsing-based statistical machine translation. The main con- tributions in this release are the...

This paper describes the development of French–English and English–French statisti- cal machine translation systems for the 2012 WMT shared task evaluation. We developed phrase-based systems based on the Moses de- …...

Machine translation aims to generate a target sentence that is semantically equivalent to the source sentence. However, most of current statistical machine translation models do not model the semantics of …...

The current state-of-the-art in statistical machine translation (SMT) suffers from is- sues of sparsity and inadequate modeling power when translating into morphologi- cally rich languages. We model both in- flection …...

Tense is a small element to a sentence, how- ever, error tense can raise odd grammars and result in misunderstanding. Recently, tense has drawn attention in many natural language processing …...

AbstractInnovations in localisation have focused on the collection and leverage of language resources. However, smaller localisation clients and Language Service Providers are poorly positioned to exploit the benefits of language …...

We present an online learning algorithm for statistical machine translation (SMT) based on stochastic gradient descent (SGD). Under the online setting of rank learning, a corpus-wise loss has to be …...

We study a problem with pairwise ranking optimization (PRO): that it tends to yield too short translations. We find that this is partially due to the inadequate smoothing in PRO’s …...

This paper presents a novel approach to pivot-based machine translation (MT): while the state-of-the-art uses two statisti- cal systems, this proposal treats the second system as a black box. Our …...

Accurate and robust metrics for automatic eval- uation are key to the development of statistical machine translation (MT) systems. We first introduce a new regression model that uses a probabilistic …...

AbstractWe propose a pre-processing stage for Statistical Machine Translation (SMT) systems where the words of the source sentence are re-ordered as per the syntax of the target language prior to …...

This paper seeks to close the gap between training algorithms used in statistical machine translation and machine learning, specifically the framework of empirical risk minimization. We review well-known algorithms, arguing …...

AbstractSubtitling and audiovisual translation have been recognized as areas that could greatly benefit from the introduction of Statistical Machine Translation (SMT) followed by post-editing, in order to increase efficiency of …...

A fundamental problem in text generation is word ordering. Word ordering is a com- putationally difficult problem, which can be constrained to some extent for particu- lar applications, for example …...

Supplementary data selection from out-of-domain or related-domain data is a well established technique in domain adaptation of statistical machine translation. The selection criteria for such data are mostly based on …...

We present the UKP system which performed best in the Semantic Textual Similarity (STS) task at SemEval-2012 in two out of three met- rics. It uses a simple log-linear regression …...

A fully discriminative training framework for Statistical Machine Translation A major pitfall of existing statistical machine translation systems is their lack of a proper training procedure. In fact, the phrase …...

This paper addresses the problem of produc- ing a diverse set of plausible translations. We present a simple procedure that can be used with any statistical machine translation (MT) system. …...

In Statistical Machine Translation we often have to combine different sources of parallel training data to build a good system. One way of doing this is to build separate translation …...

Neural network language models, or continuous-space language models (CSLMs), have been shown to improve the performance of statistical machine translation (SMT) when they are used for reranking n-best translations. However, …...

Real-word errors or context sensitive spelling errors, are misspelled words that have been wrongly converted into another word of vocabulary. One way to detect and correct real-word errors is using …...

This paper aims at effective use of training da- ta by extracting sentences from large general- domain corpora to adapt statistical machine translation systems to domain-specific data. We regard this …...

Despite being closely related languages, German and English are characterized by important word order differences. Long- range reordering of verbs, in particular, represents a real challenge for state-of-the- art SMT …...

We present a fast and scalable online method for tuning statistical machine trans- lation models with large feature sets. The standard tuning algorithm—MERT—only scales to tens of features. Recent discrimi- …...

We use feature decay algorithms (FDA) for fast deployment of accurate statistical machine translation systems taking only about half a day for each translation direc- tion. We develop parallel FDA …...

Typical statistical machine translation sys- tems are batch trained with a given train- ing data and their performances are large- ly influenced by the amount of data. With the growth …...

Since statistical machine translation (SMT) and translation memory (TM) complement each other in matched and unmatched regions, integrated models are proposed in this paper to incorporate TM information into phrase-based …...

Translation models in statistical machine translation can be scaled to large corpora and arbitrarily-long phrases by looking up translations of source phrases “on the fly” in an indexed parallel corpus …...

The many differences between Dialectal Ara- bic and Modern Standard Arabic (MSA) pose a challenge to the majority of Arabic natural language processing tools, which are designed for MSA. In …...

This paper examines tuning for statistical ma- chine translation (SMT) with respect to mul- tiple evaluation metrics. We propose several novel methods for tuning towards multiple ob- jectives, including some …...

We present a novel online learning ap- proach for statistical machine translation tailored to the computer assisted transla- tion scenario. With the...

Pronunciation dictionaries provide a readily available parallel corpus for learning to trans- duce between character strings and phoneme strings or vice versa. Translation models can be used to derive character-level …...

Research on statistical machine transla- tion has focused on particular translation directions, typically with English as the target language, e.g., from Arabic to En- glish. When we reverse the translation …...

This paper proposes a nonparametric Bayesian method for inducing Part-of- Speech (POS) tags in dependency trees to improve the performance of statistical machine translation (SMT). In particular, we extend the …...

In this paper, we propose a new Bayesian inference method to train statistical ma- chine translation systems using only non- parallel corpora. Following a probabilis- tic decipherment approach, we first …...

Community question answering (CQA) has become an increasingly popular re- search topic. In this paper, we focus on the problem of question retrieval. Question retrieval in CQA can automatically find …...

This paper describes the statistical ma- chine translation (SMT) systems devel- oped at RWTH Aachen University for the translation task of the ACL 2013 Eighth Workshop on Statistical Machine Translation …...

This paper describes T ¨UB˙ITAK-B˙ILGEM statistical machine translation (SMT) sys- tems submitted to the Eighth Work- shop on Statistical Machine Transla- tion (WMT) shared translation task for German-English language pair …...

This paper proposes a new approach to domain adaptation in statistical machine translation (SMT) based on a vector space model (VSM). The general idea is first to create a vector …...

This paper describes the English-Russian and Russian-English statistical machine translation (SMT) systems developed at Yandex School of Data Analysis for the shared translation task of the ACL 2013 Eighth Workshop …...

Previous models in syntax-based statistical machine translation usually resort to some kinds of synchronous procedures, few of these works are based on the analysis-transfer-generation methodology. In this paper, we present …...

AbstractCombining Translation Memory (TM) with Statistical Machine Translation (SMT) together has been demonstrated to be beneficial. In this paper, we present a discriminative framework which can integrate TM into SMT …...

Short Messaging Service (SMS) has become a popular form of communication. While it is predominantly used for monolingual communication, it can be extremely useful for facilitating cross-lingual communication through statistical …...

An important step in mainstream statisti- cal machine translation (SMT) is combin- ing bidirectional alignments into one align- ment model. This process is called sym- metrization. Most of the symmetrization …...

During decoding, the Statistical Machine Translation (SMT) decoder travels over all complete paths on the Search Graph (SG), seeks those with cheapest costs and back- tracks to read off the …...

This paper evaluates the impact of ma- chine translation on the software localiza- tion process and the daily work of profes- sional translators when SMT is applied to low-resourced languages …...

Although the performance of SMT sys- tems has improved over a range of differ- ent linguistic phenomena, negation has not yet received adequate treatment. Previous works have considered the prob- …...

AbstractThe training data for statistical machine translation are gathered from various sources representing a mixture of domains. In this work, we argue that when translating dialects representing varieties of the …...

This article describes a linguistically in- formed method for integrating phrasal verbs into statistical machine translation (SMT) systems. In a case study involving English to Bulgarian SMT, we show that …...

In this paper, we explore bilingual sentiment knowledge for statistical machine translation (SMT). We propose to explicitly model the consistency of sentiment between the source and target side with a …...

Class-based language modeling (LM) is a long-studied and effective approach to overcome data sparsity in the context of n-gram model training. In statistical machine translation (SMT), differ- ent forms of …...

This paper explores a number of simple and effective techniques to adapt statisti- cal machine translation (SMT) systems in the medical domain. Comparative exper- iments are conducted on large corpora …...

. If neural networks play an increasingly important role in natural language processing, training issues still hinder their dissemination in the community. This paper studies different learning strategies for neural …...

Numerous works in Statistical Machine Translation (SMT) have attempted to iden- tify better translation hypotheses obtained by an initial decoding using an improved, but more costly scoring function. In this …...

The efficacy of discriminative training in Statistical Machine Translation is heavily dependent on the quality of the develop- ment corpus used, and on its similarity to the test set. This …...

AbstractWe introduce two document-level features to polish baseline sentence-level translations generated by a state-of-the-art statistical machine translation (SMT) system. One feature uses the word-embedding technique to model the relation between …...

This paper describes adapting statistical machine translation (SMT) systems to medical domain using in-domain and general-domain data as well as web- crawled in-domain resources. In order to complement the limited …...

AbstractLeft-to-right (LR) decoding Watanabe et al. (2006) is a promising decoding algorithm for hierarchical phrase-based translation (Hiero) that visits input spans in arbitrary order producing the output translation in left …...

In this paper we improve Urdu→Hindi⇄English machine trans- lation through triangulation and translit- eration. First we built an Urdu→Hindi SMT system by inducing triangulated and transliterated phrase-tables from Urdu–English and …...

This work is concerned with incrementally training statistical machine translation (SMT) models when new data becomes available. That, in contrast to re-training new models based on the entire accumulated data. …...

For languages where space cannot be a boundary of a word, such as Chinese and Vietnamese, word segmentation is always the task to be done first in a statistical machine …...

AbstractThis paper presents a Japanese-to-English statistical machine translation system specialized for patent translation. Patents are practically useful technical documents, but their translation needs different efforts from general-purpose translation. There are …...

Phrase reordering is a challenge for statis- tical machine translation systems. Posing phrase movements as a prediction prob- lem using contextual features modeled by maximum entropy-based classifier is su- perior …...

Morphological segmentation is an effec- tive sparsity reduction strategy for statis- tical machine translation (SMT) involv- ing morphologically complex languages. When translating into a segmented lan- guage, an extra step …...

Statistical Machine Translation (SMT) usually utilizes contextual information to disambiguate translation candidates. However, it is often limited to contexts within sentence boundaries, hence broader topical information cannot be leveraged. In …...

AbstractThis paper presents a systematic human evaluation of translations of English support verb constructions produced by a rule-based machine translation (RBMT) system (OpenLogos) and a statistical machine translation (SMT) system …...

AbstractLinear models, which support efficient learning and inference, are the workhorses of statistical machine translation; however, linear decision rules are less attractive from a modeling perspective. In this work, we …...

AbstractThis article describes a large-scale evaluation of the use of Statistical Machine Translation for professional subtitling. The work was carried out within the FP7 EU-funded project SUMAT and involved two …...

Given a pair of source and target language sentences which are translations of each other with known word alignments between them, we extract bilingual phrase-level segmenta- tions of such a …...

AbstractThis work describes an experimental evaluation of the significance of phrasal verb treatment for obtaining better quality statistical machine translation (SMT) results. The importance of the detection and special treatment …...

AbstractWe present ParCor, a parallel corpus of texts in which pronoun coreference ― reduced coreference in which pronouns are used as referring expressions ― has been annotated. The corpus is …...

AbstractThis paper is a partial report of an on-going Kakenhi project which aims to improve sub-sentential alignment and release multilingual syntactic patterns for statistical and example-based machine translation. Here we …...

We propose a novel learning approach for statistical machine translation (SMT) that allows to extract supervision signals for structured learning from an extrinsic re- sponse to a translation input. We …...

AbstractIn Statistical Machine Translation (SMT), the constraints on word reorderings have a great impact on the set of potential translations that are explored. Notwithstanding computationnal issues, the reordering space of …...

Parameter tuning is an important problem in statistical machine translation, but surpris- ingly, most existing methods such as MERT, MIRA and PRO are agnostic about search, while search errors could …...

AbstractWe present a compendium of 110 Statistical Machine Translation systems built from parallel corpora of 11 Indian languages belonging to both Indo-Aryan and Dravidian families. We analyze the relationship between …...

Statistical Machine Translation (SMT) is a well-known and well established data- driven approach used for language trans- lation. The focus of this work is to de- velop a statistical machine …...

Translation between varieties of the same language is a widespread reality in the lo- calisation industry. However, monolin- gual statistical machine translation (SMT) is still a solution that has not …...

. Bilingual lexicons play a vital role in cross-language information retrieval and machine translation. The manual construction of these lexicons is often costly and time consuming. Word alignment techniques are …...

We introduce submodular optimization to the problem of training data subset selection for statistical machine translation (SMT). By explicitly formulating data selection as a submodular program, we ob- tain fast …...

Recently, syntactic information has helped significantly to improve statistical ma- chine translation. However, the use of syn- tactic information may have a negative im- pact on the speed of translation …...

State of the art statistical machine translation systems are typically trained by symmetrizing word alignments in two translation directions. We introduce a new method that improves word alignment results, based …...

AbstractWe present a large scale study of the languages spoken by bilingual workers on Mechanical Turk (MTurk). We establish a methodology for determining the language skills of anonymous crowd workers …...

AbstractIn this paper we present a Dutch and English dataset that can serve as a gold standard for evaluating text normalization approaches. With the combination of text messages, message board …...

Domain adaptation for statistical machine translation is the task of altering general models to improve performance on the test domain. In this work, we suggest several novel weighting schemes based …...

Cross-lingual learning has become a popu- lar approach to facilitate the development of resources and tools for low-density lan- guages. Its underlying idea is to make use of existing tools …...

In previous work we showed that when us- ing an SMT model trained on old-domain data to translate text in a new-domain, most errors are due to unseen source words, …...

We describe an approach to word ordering using modelling techniques from statisti- cal machine translation. The system in- corporates a phrase-based model of string generation that aims to take unordered …...

We present a theoretical analysis of online parameter tuning in statistical machine translation (SMT) from a coactive learn- ing view. This perspective allows us to give regret and generalization bounds …...

Clustering words is a widely used technique in statistical natural language processing. It re- quires syntactic, semantic, and contextual fea- tures. Especially, semantic clustering is gain- ing a lot of …...

In this paper, we describe a method based on statistical machine translation (SMT) that is able to restore accents in Hungarian texts with high accuracy. Due to the ag- glutination …...

In this paper, we explore statistical ma- chine translation (SMT) approaches to au- tomatic text simplification (ATS) for Span- ish. First, we compare the performances of the standard phrase-based (PB) …...

Current approaches to cross-lingual informa- tion retrieval (CLIR) rely on standard retrieval models into which query translations by sta- tistical machine translation (SMT) are inte- grated at varying degree. In …...

Statistical machine translation (SMT) suf- fers from various problems which are ex- acerbated where training data is in short supply. In this paper we address the data sparsity problem in …...

Developing a system that can automatically respond to a user’s utterance has recently be- come a topic of research in natural language processing. However, most works on the topic take …...

Crowdsourcing makes it possible to create translations at much lower cost than hiring professional translators. However, it is still expensive to obtain the millions of transla- tions that are needed …...

In this paper the author presents meth- ods for dynamic terminology integration in statistical machine translation systems using a source text pre-processing work- flow. The workflow consists of exchange- able …...

Word segmentation is helpful in Chinese nat- ural language processing in many aspects. However it is showed that different word seg- mentation strategies do not affect the per- formance of …...

This paper describes an Example-Based Ma- chine Translation prototype and presents an evaluation of the impact of using a domain- specific vocabulary on its performance. This prototype is based on …...

This paper presents a hybrid machine translation framework based on a pre- processor that translates fragments of the input text by using example-based ma- chine translation techniques. The pre- processor …...

Lexical selection is of great importance to statistical machine translation. In this paper, we propose a graph-based frame- work for collective lexical selection. The framework is established on a translation …...

We present an incremental adaptation ap- proach for statistical machine translation that maintains a flexible hierarchical do- main structure within a single consistent model. Both weights and rules are updated …...

The multilingual Paraphrase Database (PPDB) is a freely available automatically created resource of paraphrases in mul- tiple languages. In statistical machine translation, paraphrases can be used to provide translation for …...

Scarcity of annotated corpora for many languages is a bottleneck for training fine- grained sentiment analysis models that can tag aspects and subjective phrases. We pro- pose to exploit statistical …...

Translation memories (TM) are widely used in the localization industry to im- prove consistency and speed of human translation. Several approaches have been presented to integrate the bilingual trans- lation …...

We present our work on leveraging multilin- gual parallel corpora of small sizes for Sta- tistical Machine Translation between Japanese and Hindi using multiple pivot languages. In our setting, the …...

The lack of parallel data for many language pairs is an important challenge to statistical ma- chine translation (SMT). One common solution is to pivot through a third language for …...

Parameter tuning is a key problem for sta- tistical machine translation (SMT). Most popular parameter tuning algorithms for SMT are agnostic of decoding, result- ing in parameters vulnerable to search …...

This paper describes the importance of in- troducing a phrase-based language model in the process of machine translation. In fact, nowadays SMT are based on phrases for translation but their …...

Translated texts (in any language) are so markedly different from original ones that text classification techniques can be used to tease them apart. Previous work has shown that awareness to …...

This paper describes the statistical machine translation system developed at RWTH Aachen University for the German→English translation task of the EMNLP 2015 Tenth Workshop on Statistical Machine Translation (WMT 2015). …...

One of the major challenges for statisti- cal machine translation (SMT) is to choose the appropriate translation rules based on the sentence context. This paper pro- poses a continuous space …...

We present a corpus of 2,380 natural language queries paired with machine readable formu- lae that can be executed against world wide geographic data of the OpenStreetMap (OSM) database. We …...

An important aspect for the task of grammat- ical error correction (GEC) that has not yet been adequately explored is adaptation based on the native language (L1) of writers, despite …...

. This paper proposes a new discriminative framework to train continuous-space trans- lation models based. This framework relies on the definition of a new objective function that al- lows us …...

AbstractStatistical Machine Translation (SMT) of highly inflected, low-resource languages suffers from the problem of low bitext availability, which is exacerbated by large inflectional paradigms. When translating into English, rich source …...

AbstractParallel sentence representations are important for bilingual and cross-lingual tasks in natural language processing. In this paper, we explore a bilingual autoencoder approach to model parallel sentences. We extract sentence-level …...

AbstractWe construct a case-based English-to-Chinese semantic constituent parallel Treebank for a Statistical Machine Translation (SMT) task by labelling each node of the Deep Syntactic Tree (DST) with our refined semantic …...

AbstractMethods for text simplification using the framework of statistical machine translation have been extensively studied in recent years. However, building the monolingual parallel corpus necessary for training the model requires …...

We describe the systems entered by the National Research Council Canada in the SemEval-2016 Task1: Crosslingual Semantic Textual Similarity. We tried two approaches: One computes a true crosslingual similarity based …...

AbstractAlthough more additional corpora are now available for Statistical Machine Translation (SMT), only the ones which belong to the same or similar domains of the original corpus can indeed enhance …...

Research on grammatical error correction has received considerable attention. For dealing with all types of errors, grammatical error cor- rection methods that employ statistical ma- chine translation (SMT) have been …...

AbstractThis paper discusses the role that statistical machine translation (SMT) can play in the development of cross-border EU e-commerce,by highlighting extant obstacles and identifying relevant technologies to overcome them. In …...

AbstractWe present a new Bayesian HMM word alignment model for statistical machine translation. The model is a mixture of an alignment model and a language model. The alignment component is …...

AbstractLong-distance semantic dependencies are crucial for lexical choice in statistical machine translation. In this paper, we study semantic dependencies between verbs and their arguments by modeling selectional preferences in the …...

AbstractSelecting appropriate translations for source words with multiple meanings still remains a challenge for statistical machine translation (SMT). One reason for this is that most SMT systems are not good …...

AbstractWe describe IRIS, a statistical machine translation (SMT) system for translating from English into Irish and vice versa. Since Irish is considered an under-resourced language with a limited amount of …...

AbstractIn this paper we present our work on the usage of lexical resources for the Machine Translation English and Malayalam. We describe a comparative performance between different Statistical Machine Translation …...

AbstractResearch in statistical machine translation (SMT) is largely driven by formal translation tasks, while translating informal text is much more challenging. In this paper we focus on SMT for the …...

We present an approach to improve sta- tistical machine translation of image de- scriptions by multimodal pivots defined in visual space. The key idea is to perform image retrieval over …...

AbstractWe propose named entity abstraction methods with fine-grained named entity labels for improving statistical machine translation (SMT). The methods are based on a bilingual named entity recognizer that uses a …...

Automatically recognising medical con- cepts mentioned in social media messages (e.g. tweets) enables several applications for enhancing health quality of people in a community, e.g. real-time monitoring of infectious diseases …...

AbstractMost recent sentence simplification systems use basic machine translation models to learn lexical and syntactic paraphrases from a manually simplified parallel corpus. These methods are limited by the quality and …...

AbstractOut-of-vocabulary (OOV) word is a crucial problem in statistical machine translation (SMT) with low resources. OOV paraphrasing that augments the translation model for the OOV words by using the translation …...

AbstractThis paper proposes a method for improving the results of a statistical Machine Translation system using boundedness, a pragmatic component of the verbal phrase’s lexical aspect. First, the paper presents …...

While end-to-end neural machine transla- tion (NMT) has made remarkable progress recently, NMT systems only rely on par- allel corpora for parameter estimation. Since parallel corpora are usually limited in …...

AbstractThis work describes the statistical machine translation (SMT) systems of RWTH Aachen University developed for the evaluation campaign of International Workshop on Spoken Language Translation (IWSLT) 2016. We have participated …...

AbstractOut-of-vocabulary words (OOVs) are a ubiquitous and difficult problem in statistical machine translation (SMT). This paper studies different strategies of using BabelNet to alleviate the negative impact brought about by …...

AbstractIn this paper, we propose a novel supervised model for parsing natural language sentences into their formal semantic representations. This model treats sentence-to-lambda-logical expression conversion within the framework of the …...

Named Entities (NEs) are a very important part of a sentence and their treatment is a potentially useful preprocessing step for Statistical Machine Translation (SMT). Improper translation of NE lapse …...

AbstractThe goal of counterfactual learning for statistical machine translation (SMT) is to optimize a target SMT system from logged data that consist of user feedback to translations that were predicted …...

Linguistic resources such as part-of- speech (POS) tags have been extensively used in statistical machine translation (SMT) frameworks and have yielded bet- ter performances. However, usage of such linguistic annotations …...

AbstractIn this paper, we implement a multilingual Statistical Machine Translation (SMT) system for Arabic-English Translation. Arabic Text can be categorized into standard and dialectal Arabic. These two forms of Arabic …...

AbstractOne of the main obstacles for many Digital Humanities projects is the low data availability. Texts have to be digitized in an expensive and time consuming process whereas Optical Character …...

In this paper, we discuss different methods which use meta information and richer context that may accompany source language input to improve machine translation quality. We focus on category information …...

AbstractIn this paper, we introduce a hybrid search for attention-based neural machine translation (NMT). A target phrase learned with statistical MT models extends a hypothesis in the NMT beam search …...

AbstractLearning internal word structure has recently been recognized as an important step in various multilingual processing tasks and in theoretical language comparison. In this paper, we present a neural encoder-decoder …...

In this paper, we attempt to improve Statistical Machine Translation (SMT) systems between Czech, Latvian and English in WNT’17 News translation task. We also participated in the Biomedical task and …...

Statistical machine translation (SMT) sys- tems use local cues from n-gram trans- lation and language models to select the translation of each source word. Such systems do not explicitly perform …...

Neural Machine Translation (NMT) has attracted increasing attention in the recent years. However, it tends to require very large training corpora which could prove problematic for languages with low resources. …...

Parallel data are an important part of a reliable Statistical Machine Translation (SMT) system. The more of these data are available, the better the quality of the SMT system. However, …...

AbstractWhile Neural Machine Translation (NMT) technology has been around for a few years now in research and development, it is still in its infancy when it comes to customization readiness …...

AbstractA prerequisite for training corpus-based machine translation (MT) systems – either Statistical MT (SMT) or Neural MT (NMT) – is the availability of high-quality parallel data. This is arguably more …...

Statistical Machine Translation (SMT) approaches fails to handle the rich morphology when translating into morphologically rich language. This is due to the data sparsity, which is the missing of the …...

AbstractIn this paper, we describe an attempt towards the development of parallel corpora for English and Ethiopian Languages, such as Amharic, Tigrigna, Afan-Oromo, Wolaytta and Ge’ez. The corpora are used …...

AbstractThis paper reports on a pilot experiment that compares two different machine translation (MT) paradigms in reading comprehension tests. To explore a suitable methodology, we set up a pilot experiment …...

AbstractThe lack of a spelling convention in historical documents makes their orthography to change depending on the author and the time period in which each document was written. This represents …...

AbstractNeural Machine Translation (NMT) models achieve their best performance when large sets of parallel data are used for training. Consequently, techniques for augmenting the training set have become popular recently. …...

AbstractWord alignments identify translational correspondences between words in a parallel sentence pair and is used, for instance, to learn bilingual dictionaries, to train statistical machine translation systems, or to perform …...

AbstractGrammatical error correction (GEC) is one of the areas in natural language processing in which purely neural models have not yet superseded more traditional symbolic models. Hybrid systems combining phrase-based …...

AbstractThe NAIST Lang-8 Learner Corpora (Lang-8 corpus) is one of the largest second-language learner corpora. The Lang-8 corpus is suitable as a training dataset for machine translation-based grammatical error correction …...

AbstractWe present a thorough comparison of two principal approaches to Cross-Lingual Information Retrieval: document translation (DT) and query translation (QT). Our experiments are conducted using the cross-lingual test collection produced …...

Translating text that diverges from the training domain is a key challenge for machine trans- lation. Domain robustness—the generalization of models to unseen test domains—is low for both statistical (SMT) …...

AbstractThe Transformer translation model (Vaswani et al., 2017) based on a multi-head attention mechanism can be computed effectively in parallel and has significantly pushed forward the performance of Neural Machine …...

AbstractData-driven segmentation of words into subword units has been used in various natural language processing applications such as automatic speech recognition and statistical machine translation for almost 20 years. Recently …...

AbstractIn this paper the MTUOC project, aiming to provide an easy integration of neural and statistical machine translation systems, is presented. Almost all the required software to train and use …...

Because sign language is a visual language, the translation of it into spoken language is typically performed through an intermediate representation called gloss notation. In sign language, function words, such …...

AbstractWeighted pushdown automata (WPDAs) are at the core of many natural language processing tasks, like syntax-based statistical machine translation and transition-based dependency parsing. As most existing dynamic programming algorithms are …...

AbstractThis paper describes the acquisition, preprocessing, segmentation, and alignment of an Amharic-English parallel corpus. It will be helpful for machine translation of a low-resource language, Amharic. We freely released the …...

AbstractThis article presents a system to generate Arab music improvisation using machine translation. To reach this goal, we developed a machine translation model to translate a vocal improvisation into an …...