Translation Quality

We are developing stylistic grammars to provide the basis for a French and English stylistic parser. Our stylistic grammar is a branching stratificational model, built upon a foundation dealing with …...

This paper proposes a method that automatically acquires the SAs (semantic attributes) of user defined words. Applying this method to the compilation of a user dictionary targeting newspaper article sentences …...

We present results of experiments designed to assess the usefulness of a new technique for the evaluation of translation quality, comparing human rankings with automatic measures. The basis of our …...

The previous English-Korean MT system that was the transfer-based MT system and applied to only written text enumerated a following brief list of the problems that had not seemed to …...

AbstractWe describe methods for translating a text given in multiple source languages into a single target language. The goal is to improve translation quality in applications where the ultimate goal …...

. The first research on integrating controlled language data in an Example-Based Machine Translation (EBMT) system was published in [Gough & Way, 2003]. We improve on their sub-sentential alignment algorithm …...

. 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 …...

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 …...

We begin by exploring theoretical and practical issues with phrasal SMT, several of which are addressed by syntax-based SMT. Next, to address problems not handled by syntax, we propose the …...

AbstractIn syntax-directed translation, the source-language input is first parsed into a parse-tree, which is then recursively converted into a string in the target-language. We model this conversion by an extended …...

AbstractState-of-the-art statistical machine translation is based on alignments between phrases – sequences of words in the source and target sentences. The learning step in these systems often relies on alignments …...

This paper presents an empirical study on how different selections of input translation systems affect translation quality in system combination. We give empirical evidence that the systems to be combined …...

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 …...

Syntactic word reordering is essential for translations across different grammar struc- tures between syntactically distant language- pairs. In this paper, we propose to em- bed local and non-local word reordering …...

. n-gram language modeling is a popular technique used to improve performance of various NLP applications. However, it still faces the “curse of dimensionality” issue wherein word sequences on which …...

While the intuition that morphological pre- processing of languages in various applica- tions can be beneficial appears to be often true, especially in the case of morphologi- cally richer languages, …...

We introduce alignment models for Machine Trans- lation that take into account the context of a source word when determining its translation. Since the use of these contexts alone causes …...

This paper investigates the idea of adapt- ing language models for phrases that have poor translation quality. We apply a se- lective adaptation criterion which uses a classifier to locate …...

In this paper we compare and contrast two approaches to Machine Translation (MT): the CMU-UKA Syntax Augmented Machine Translation system (SAMT) and UPC-TALP N-gram-based Statistical Ma- chine Translation (SMT). SAMT …...

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 …...

We describe the system used in our sub- mission to the WMT-2009 French-English translation task. We use the Moses phrase- based Statistical Machine Translation sys- tem with two simple modications …...

This paper proposes the use of a Determin- istic Annealing Expectation-Maximization (DAEM) algorithm to estimate the word- alignments involved in the statistical trans- lation process. This approach is aimed to …...

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 …...

AbstractWith the development of the Internet environments, more and more language services become accessible for common people. However, the gap between human translators and machine translators remains huge especially for …...

Cross-language document summarization is a task of producing a summary in one language for a document set in a different language. Ex- isting methods simply use machine translation for document …...

We report on investigations into hierarchi- cal phrase-based translation grammars based on rules extracted from posterior distributions over alignments of the parallel text. Rather than restrict rule extraction to a …...

Targeted paraphrasing is a new approach to the problem of obtaining cost-effective, reasonable quality translation that makes use of simple and inexpensive human computations by monolin- gual speakers in combination …...

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 …...

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 …...

UPV-PRHLT participated in the System Combination task of the Fifth Workshop on Statistical Machine Translation (WMT 2010). On each translation direction, all the submitted systems were combined into a consensus …...

AbstractTranslation into the languages with relatively free word order has received a lot less attention than translation into fixed word order languages (English), or into analytical languages (Chinese). At the …...

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 …...

In this work we propose methods to label probabilistic synchronous context-free gram- mar (PSCFG) rules using only word tags, generated by either part-of-speech analysis or unsupervised word class induction. The …...

In this paper, we present a new hybridisa- tion approach consisting of enriching the phrase table of a phrase-based statistical machine translation system with bilingual phrase pairs matching structural transfer …...

MonoTrans2 is a translation system that com- bines machine translation (MT) with human computation using two crowds of monolin- gual source (Haitian Creole) and target (En- glish) speakers. We report …...

In past Evaluations for Machine Translation of European Languages, it could be shown that the translation performance of SMT systems can be increased by integrating a bilingual lan- guage model …...

One of the main issues in a word alignment task is the difficulty of handling function words that do not have direct translations which we call unique function words. They …...

Conventional rule-based machine translation system suffers from its weakness of fluency in the view of target language generation. In particular, when translating English spoken language to Korean, the fluency of …...

AbstractMultiWord Expressions (MWEs) repesent a key issue for numerous applications in Natural Language Processing (NLP) especially for Machine Translation (MT). In this paper, we describe a strategy for detecting translation …...

We report on an experiment to test the ef- ficacy of ‘controlled language’ authoring of technical documents in Japanese, with respect both to the readability of the Jap- anese source …...

The usefulness of a translated text for gist- ing purposes strongly depends on the over- all translation quality of the text, but espe- cially on the translation quality of the …...

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 …...

Automatically clustering words from a mono- lingual or bilingual training corpus into classes is a widely used technique in statisti- cal natural language processing. We present a very simple and …...

Currently, almost all of the statistical ma- chine translation (SMT) models are trained with the parallel corpora in some specific domains. However, when it comes to a lan- guage pair …...

Domain adaptation for SMT usually adapts models to an individual specific domain. However, it often lacks some correlation among different domains where common knowledge could be shared to improve the …...

Compared to the edited genres that have played a central role in NLP research, mi- croblog texts use a more informal register with nonstandard lexical items, abbreviations, and free orthographic …...

We introduce a social media text normal- ization system that can be deployed as a preprocessing step for Machine Transla- tion and various NLP applications to han- dle social media …...

Translation into morphologically rich lan- guages is an important but recalcitrant prob- lem in MT. We present a simple and effec- tive approach that deals with the problem in two …...

This paper demonstrates the need and im- pact of subcategorization information for SMT. We combine (i) features on source- side syntactic subcategorization and (ii) an external knowledge base with quantita- …...

AbstractThe user-generated content represents an increasing share of the information available today. To make this type of content instantly accessible in another language, the ACCEPT project focuses on developing pre-editing …...

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 …...

Crowdsourcing is a viable mechanism for creating training data for machine trans- lation. It provides a low cost, fast turn- around way of processing large volumes of data. However, when …...

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 …...

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 …...

We introduce new features for incorpo- rating semantic predicate-argument struc- tures in machine translation (MT). The methods focus on the completeness of the semantic structures of the translations, as well …...

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 …...

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 …...

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 …...

AbstractWe present a first attempt at predicting the quality of translations produced by human, professional translators. We examine datasets annotated for quality at sentence- and word-level for four language pairs …...

In this paper, we propose a recurrent neural network-based tuple sequence model (RNNTSM) that can help phrase-based translation model overcome the phrasal independence assumption. Our RNNTSM can potentially capture arbitrary …...

AbstractSince the effectiveness of MT adaptation relies on the text repetitiveness, the question on how to measure repetitions in a text naturally arises. This work deals with the issue of …...

This study investigates on building a better Chinese word segmentation mod- el for statistical machine translation. aims at leveraging word boundary infor-It mation, automatically learned by bilin- gual character-based alignments, …...

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 …...

We propose the use of neural networks to model source-side preordering for faster and better statistical machine translation. The neu- ral network trains a logistic regression model to predict whether …...

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 …...

This work explores the application of re- current neural network (RNN) language and translation models during phrase- based decoding. Due to their use of un- bounded context, the decoder integration …...

Following the idea of using distributed semantic representations to facilitate the computation of semantic similarity between translation equivalents, we propose a novel framework to learn bilingual distributed phrase representations for …...

We present a novel approach for unsu- pervised induction of a Reordering Gram- mar using a modified form of permuta- tion trees (Zhang and Gildea, 2007), which we apply to …...

AbstractThis paper presents the evaluation of the translation quality and Cross-Lingual Information Retrieval (CLIR) performance when using session information as the context of queries. The hypothesis is that previous queries …...

This paper presents an study of the use of in- terlocking phrases in phrase-based statistical machine translation. We examine the effect on translation quality when the translation units used in …...

AbstractIn this paper, we propose a new decoding method for phrase-based statistical machine translation which directly uses multiple preordering candidates as a graph structure. Compared with previous phrase-based decoding methods, …...

Interactive machine translation research has focused primarily on predictive typing, which re- quires a human to type parts of the translation. This paper explores an interactive setting in which humans …...

AbstractThis paper proposes three distortion models to explicitly incorporate the word reordering knowledge into attention-based Neural Machine Translation (NMT) for further improving translation performance. Our proposed models enable attention mechanism …...

AbstractWe present Grid Beam Search (GBS), an algorithm which extends beam search to allow the inclusion of pre-specified lexical constraints. The algorithm can be used with any model which generates …...

The WMT17 Neural Machine Translation Training Task aims to test various methods of training neural machine translation sys- tems. We describe the AFRL submission, including preprocessing and its knowledge distillation …...

Social media platforms such as Twitter and Facebook are hugely popular websites through which Internet users can communicate and spread information worldwide. On Twitter, mes- sages (tweets) are generated by …...

We explore ways of identifying terms from monolingual texts and integrate them into investigating the contribution of terminology to translation quality.The researchers proposed a supervised learning method using common statistical …...

The past several years have witnessed the rapid progress of end-to-end Neural Machine Translation (NMT). However, there exists discrepancy between training and inference in NMT when decoding, which may lead …...

We applied a method for automatic extraction of bilingual multiword units (BMWUs) from a parallel corpus in order to investigate their contribution to translation quality in terms of adequacy and …...

AbstractExisting neural machine translation systems do not explicitly model what has been translated and what has not during the decoding phase. To address this problem, we propose a novel mechanism …...

AbstractMultilingual machine translation addresses the task of translating between multiple source and target languages. We propose task-specific attention models, a simple but effective technique for improving the quality of sequence-to-sequence …...

This paper describes an approach for translation between Chinese dialects that can produce target sentences at different registers. We focus on Mandarin as the source language, and Cantonese as the …...

AbstractPrevious attempts at injecting semantic frame biases into SMT training for low resource languages failed because either (a) no semantic parser is available for the low resource input language; or …...

AbstractTransfer learning approaches for Neural Machine Translation (NMT) train a NMT model on an assisting language-target language pair (parent model) which is later fine-tuned for the source language-target language pair …...

AbstractNeural Machine Translation (NMT) has achieved notable success in recent years. Such a framework usually generates translations in isolation. In contrast, human translators often refer to reference data, either rephrasing …...

AbstractThe APE-QUEST project (2018–2020) sets up a quality gate and crowdsourcing workflow for the eTranslation system of EC’s Connecting Europe Facility to improve translation quality in specific domains. It packages …...

AbstractQuery translation (QT) serves as a critical factor in successful cross-lingual information retrieval (CLIR). Due to the lack of parallel query samples, neural-based QT models are usually optimized with synthetic …...

AbstractIn this paper, we deal with two problems in Japanese-English machine translation of news articles. The first problem is the quality of parallel corpora. Neural machine translation (NMT) systems suffer …...

AbstractIn this study, linguistic knowledge at different levels are incorporated into the neural machine translation (NMT) framework to improve translation quality for language pairs with extremely limited data. Integrating manually …...

Current neural translation networks are based on an effective attention mechanism that can be considered as an implicit probabilistic notion of alignment. Such architectures do not guarantee a high quality …...

AbstractMulti-source neural machine translation aims to translate from parallel sources of information (e.g. languages, images, etc.) to a single target language, which has shown better performance than most one-to-one systems. …...

AbstractTransfer learning is a popular strategy to improve the quality of low-resource machine translation. For an optimal transfer of the embedding layer, the child and parent model should share a …...

AbstractMany studies have confirmed that translated texts exhibit different features than texts originally written in the given language. This work explores texts translated by different translators taking into account expertise …...

AbstractWe conduct in this work an evaluation study comparing offline and online neural machine translation architectures. Two sequence-to-sequence models: convolutional Pervasive Attention (Elbayad et al. 2018) and attention-based Transformer (Vaswani …...

AbstractPosition encoding (PE), an essential part of self-attention networks (SANs), is used to preserve the word order information for natural language processing tasks, generating fixed position indices for input sequences. …...

AbstractRecent studies have proven that the training of neural machine translation (NMT) can be facilitated by mimicking the learning process of humans. Nevertheless, achievements of such kind of curriculum learning …...

AbstractThe Sumerian cuneiform script was invented more than 5,000 years ago and represents one of the oldest in history. We present the first attempt to translate Sumerian texts into English …...

AbstractNeural machine translation (NMT) has proven to be facilitated by curriculum learning which presents examples in an easy-to-hard order at different training stages. The keys lie in the assessment of …...

AbstractThe present article discusses how to improve translation quality when using limited training data to translate towards morphologically rich languages. The starting point is a neural MT system, used to …...

AbstractWhile it has been shown that Neural Machine Translation (NMT) is highly sensitive to noisy parallel training samples, prior work treats all types of mismatches between source and target as …...

Simultaneous speech-to-text translation is widely useful in many scenarios. The con- ventional cascaded approach uses a pipeline of streaming ASR followed by simultaneous MT, but suffers from error propagation and …...

AbstractWe revisit the topic of translation direction in the data used for training neural machine translation systems and focusing on a real-world scenario with known translation direction and imbalances in …...

AbstractThe paper presents experiments in neural machine translation with lexical constraints into a morphologically rich language. In particular and we introduce a method and based on constrained decoding and which …...

AbstractSelf-training has proven effective for improving NMT performance by augmenting model training with synthetic parallel data. The common practice is to construct synthetic data based on a randomly sampled subset …...

AbstractUsing end-to-end models for speech translation (ST) has increasingly been the focus of the ST community. These models condense the previously cascaded systems by directly converting sound waves into translated …...

AbstractIn a real-time simultaneous translation setting and neural machine translation (NMT) models start generating target language tokens from incomplete source language sentences and making them harder to translate and leading …...

AbstractIn simultaneous machine translation, finding an agent with the optimal action sequence of reads and writes that maintain a high level of translation quality while minimizing the average lag in …...

AbstractMachine translation systems are expected to cope with various types of constraints in many practical scenarios. While neural machine translation (NMT) has achieved strong performance in unconstrained cases, it is …...

AbstractSemi-parametric models, which augment generation with retrieval, have led to impressive results in language modeling and machine translation, due to their ability to retrieve fine-grained information from a datastore of …...

AbstractIn this work, we propose a Named Entity handling approach to improve translation quality within an existing Natural Language Processing (NLP) pipeline without modifying the Neural Machine Translation (NMT) component. …...

AbstractNon-autoregressive translation (NAT) models, which eliminate the sequential dependencies within the target sentence, have achieved remarkable inference speed, but suffer from inferior translation quality. Towards exploring the underlying causes, we …...

AbstractAfter its introduction the Transformer architecture quickly became the gold standard for the task of neural machine translation. A major advantage of the Transformer compared to previous architectures is the …...

AbstractLexically constrained neural machine translation (NMT) draws much industrial attention for its practical usage in specific domains. However, current autoregressive approaches suffer from high latency. In this paper, we focus …...

AbstractWe participated in the WMT 2022 Large-Scale Machine Translation Evaluation for the African Languages Shared Task. This work describes our approach, which is based on filtering the given noisy data …...

AbstractSyntactic structure has long been argued to be potentially useful for enforcing accurate word alignment and improving generalization performance of machine translation. Unfortunately, existing wisdom demonstrates its significance by considering …...

AbstractIn this paper we evaluate the utility of large language models (LLMs) for translation of text with markup in which the most important and challenging aspect is to correctly transfer …...

AbstractThe recently released NLLB-200 is a set of multilingual Neural Machine Translation models that cover 202 languages. The largest model is based on a Mixture of Experts architecture and achieves …...