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Algorithms for the alignment of words in translated texts are well established. How- ever, only recently new approaches have been proposed to identify word translations from non-parallel or even unrelated …...

In this paper, a method for the word alignment of English-Chinese corpus based on chunks is proposed. The chunks of English sentences are identified firstly. Then the chunk boundaries of …...

Word alignment plays a crucial role in sta- tistical machine translation. Word-aligned corpora have been found to be an excellent source of translation-related knowledge. We present a statistical model for …...

In this paper, a word alignment ap- proach is presented which is based on a combination of clues. Word align- ment clues indicate associations be- tween words and phrases. They …...

In this paper we report ongoing work on developing an interactive word alignment environment that will assist a user to quickly produce accurate full-coverage word alignment in bitexts for different …...

Aligning words from sentences which are mutual translations is an important problem in different set- tings, such as bilingual terminology extraction, Ma- chine Translation, or projection of linguistic fea- tures. …...

This system is an experiment of examples based approach. It is based on a corpus containing 220 thousand sentence pairs with word alignment. The system contains four parts: matching and …...

The paper presents a method for word sense disambiguation based on parallel corpora. The method exploits recent advances in word alignment and word clustering based on automatic extraction of translation …...

In this paper, we address the word alignment problem for statistical machine translation. We aim at creating a sym- metric word alignment allowing for reli- able one-to-many and many-to-one word …...

We present a discriminative, large- margin approach to feature-based matching for word alignment. In this framework, pairs of word tokens re- ceive a matching score, which is based on features …...

This paper presents a maximum entropy word alignment algorithm for Arabic- English based on supervised training data. We demonstrate that it is feasible to cre- ate training material for problems …...

This paper proposes an alignment adaptation approach to improve domain-specific (in-domain) word alignment. The basic idea of alignment adaptation is to use out-of-domain corpus to improve in-domain word alignment results. …...

. This paper proposes an approach to improve statistical word align- ment with ensemble methods. Two ensemble methods are investigated: bagging and cross-validation committees. On these two methods, both weighted …...

This paper presents a new approach to combining outputs of existing word align- ment systems. Each alignment link is rep- resented with a set of feature functions extracted from linguistic …...

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 …...

. This paper presents and evaluates an original approach to automatically align bitexts at the word level. It relies on a syntactic dependency analysis of the texts and uses a …...

Word alignment using recency-vector based approach has recently become pop- ular. One major advantage of these tech- niques is that unlike other approaches they perform well even if the size …...

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 …...

Word alignment is the problem of annotating parallel text with translational correspon- dence. Previous generative word alignment models have made structural assumptions such as the 1-to-1, 1-to-N, or phrase-based consecutive …...

We present a general framework to incor- porate prior knowledge such as heuristics or linguistic features in statistical generative word alignment models. Prior knowledge plays a role of probabilistic soft …...

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 …...

Extracting tree transducer rules for syntac- tic MT systems can be hindered by word alignment errors that violate syntactic corre- spondences. We propose a novel model for unsupervised word alignment …...

. We present the approach we adopted to carry out a system oriented evaluation in the context of the development of a word alignment system, ALIBI. We look at three …...

We combine the strengths of Bayesian mod- eling and synchronous grammar in unsu- pervised learning of basic translation phrase pairs. The structured space of a synchronous grammar is a natural …...

Most of the current Chinese word alignment tasks often adopt word segmentation systems firstly to identify words. However, word-mismatching problems exist between languages and will degrade the performance of word …...

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 …...

We propose a version of Inversion Transduction Grammar (ITG) model with IBM-style notation of fertility to improve word-alignment performance. In our approach, binary context-free grammar rules of the source language, …...

The (batch) EM algorithm plays an important role in unsupervised induction, but it some- times suffers from slow convergence. In this paper, we show that online variants (1) provide significant …...

We present a word substitution approach to combine the output of different machine translation systems. Using part of speech information, candidate words are deter- mined among possible translation options, which …...

Syntactic reordering on the source-side is an effective way of handling word or- der differences. The � (DE) construc- tion is a flexible and ubiquitous syntac- tic structure in Chinese …...

AbstractThis paper profiles the Europarl part of an English-Swedish parallel corpus and compares it with three other subcorpora of the same parallel corpus. We first describe our method for comparison …...

This paper proposes a dependency parsing method that uses bilingual constraints to improve the accuracy of parsing bilingual texts (bitexts). In our method, a target- side tree fragment that corresponds …...

This paper describes the CMU entry for the system combination shared task at WMT’10. Our combination method is hy- pothesis selection, which uses information from n-best lists from the input …...

Word alignment plays a central role in statisti- cal MT (SMT) since almost all SMT systems extract translation rules from word aligned parallel training data. While most SMT systems use …...

AbstractThis paper first describes an experiment to construct an English-Chinese parallel corpus, then applying the Uplug word alignment tool on the corpus and finally produce and evaluate an English-Chinese word …...

We employ Maximum Entropy model to con- duct sub-tree alignment between bilingual phrasal structure trees. Various lexical and structural knowledge is explored to measure the syntactic similarity across Chinese-English bi- …...

This paper proposes a novel semi- supervised word alignment technique called EMDC that integrates discrimina- tive and generative methods. A discrim- inative aligner is used to find high preci- sion …...

AbstractIncorporating linguistic knowledge into word alignment is becoming increasingly important for current approaches in statistical machine translation research. To improve automatic word alignment and ultimately machine translation quality, an annotation …...

AbstractWe present a heuristic method for word alignment, which is the task of identifying corresponding words in parallel text. The heuristic method is based on parallel phrases extracted from manually …...

We present a simple yet powerful hier- archical search algorithm for automatic word alignment. Our algorithm induces a forest of alignments from which we can efficiently extract a ranked k-best …...

We show how features can easily be added to standard generative models for unsuper- vised learning, without requiring complex new training methods. In particular, each component multinomial of a generative …...

AbstractThis paper describes an approach based on word alignment on parallel corpora, which aims at facilitating the lexicographic work of dictionary building. Although this method has been widely used in …...

We present an unsupervised model for joint phrase alignment and extraction using non- parametric Bayesian methods and inversion transduction grammars (ITGs). The key con- tribution is that phrases of many …...

We introduce a discriminatively trained, glob- ally normalized, log-linear variant of the lex- ical translation models proposed by Brown et al. (1993). In our model, arbitrary, non- independent features may …...

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 …...

AbstractWe present a method for improving word alignment quality for phrase-based statistical machine translation by reordering the source text according to the target word order suggested by an initial word …...

AbstractThis paper presents two annotated corpora for word alignment between Japanese and English. We annotated on top of the IWSLT-2006 and the NTCIR-8 corpora. The IWSLT-2006 corpus is in the …...

AbstractWe have been creating large-scale manual word alignment corpora for Arabic-English and Chinese-English language pairs in genres such as newsire, broadcast news and conversation, and web blogs. We are now …...

AbstractThe aim of our software presentation is to demonstrate that corpus-driven bilingual dictionaries generated fully by automatic means are suitable for human use. Need for such dictionaries shows up specifically …...

AbstractThe extraction of dictionaries from parallel text corpora is an established technique. However, as parallel corpora are a scarce resource, in recent years the extraction of dictionaries using comparable corpora …...

The training of most syntactic SMT approaches involves two essential components, word alignment and monolingual parser. In the current state of the art these two components are mutually independent, thus …...

This paper presents an identification framework for extracting Tibetan base noun phrase (NP). The framework includes two phases. In the first phase, Chinese base NPs are extracted from all Chinese …...

We present a general framework containing a graded spectrum of Expectation Maximization (EM) algorithms called Unified Expectation Maximization (UEM.) UEM is parameterized by a single parameter and covers existing al- …...

AbstractWord alignment is an important step for machine translation systems. Although the alignment performance between grammatically similar languages is reported to be very high in many studies, the case is …...

We apply multi-rate HMMs, a tree struc- tured HMM model, to the word-alignment problem. Multi-rate HMMs allow us to model reordering at both the morpheme level and the word level …...

The use of pivot languages and word- alignment techniques over bilingual cor- pora has proved an effective approach for extracting paraphrases of words and short phrases. However, inherent ambiguities in …...

We propose a Name-aware Machine Translation (MT) approach which can tightly integrate name processing into MT model, by jointly annotating parallel cor- pora, extracting name-aware translation grammar and rules, adding …...

We propose a flexible and effective frame- work for extracting a bilingual dictionary from comparable corpora. Our approach is based on a novel combination of topic modeling and word alignment …...

AbstractIn this paper we present the evaluation results for the creation of WordNets for five languages (Spanish, French, German, Italian and Portuguese) using an approach based on parallel corpora. We …...

Inspired by previous work, where decipher- ment is used to improve machine translation, we propose a new idea to combine word align- ment and decipherment into a single learning process. …...

Previous studies of the effect of word alignment on translation quality in SMT generally explore link level metrics only and mostly do not show any clear connec- tions between alignment …...

AbstractIn this paper, we present a method of improving the accuracy of machine translation evaluation of Czech sentences. Given a reference sentence, our algorithm transforms it by targeted paraphrasing into …...

We show that semantic relationships can be used to improve word alignment, in ad- dition to the lexical and syntactic features that are typically used. In this paper, we present …...

Discourse connectives (e.g. however, because) are terms that explicitly express discourse rela- tions in a coherent text. While a list of discourse connectives is useful for both theoretical and empirical …...

AbstractRecent years have seen an increased interest in and availability of parallel corpora. Large corpora from international organizations (e.g. European Union, United Nations, European Patent Office), or from multilingual Internet …...

Widely used in speech and language pro- cessing, Kneser-Ney (KN) smoothing has consistently been shown to be one of the best-performing smoothing methods. However, KN smoothing assumes integer counts, limiting …...

Languages that have no explicit word de- limiters often have to be segmented for sta- tistical machine translation (SMT). This is commonly performed by automated seg- menters trained on manually …...

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 present a novel approach to im- prove word alignment for statistical ma- chine translation (SMT). Conventional word alignment methods allow discontin- uous alignment, meaning that a source (or target) …...

AbstractReordering poses a big challenge in statistical machine translation between distant language pairs. The paper presents how reordering between distant language pairs can be handled efficiently in phrase-based statistical machine …...

As conventional word alignment search algorithms usually ignore the consistency constraint in translation rule extraction, improving alignment accuracy does not necessarily increase translation quality. We propose to use coverage, which …...

Semantic parsing maps a sentence in natu- ral language into a structured meaning rep- resentation. Previous studies show that se- mantic parsing with synchronous context- free grammars (SCFGs) achieves favor- …...

We present a multi-task learning approach that jointly trains three word alignment mod- els over disjoint bitexts of three languages: source, target and pivot. Our approach builds upon model triangulation, …...

This paper presents a method for cleaning and evaluating parallel corpora using word alignments and machine learning algorithms. It is based on the assumption that parallel sentences have many word …...

AbstractCurrent methods for word alignment require considerable amounts of parallel text to deliver accurate results, a requirement which is met only for a small minority of the world’s approximately 7,000 …...

AbstractAnnotation projection is a practical method to deal with the low resource problem in incident languages (IL) processing. Previous methods on annotation projection mainly relied on word alignment results without …...

AbstractEstimating similarities at different levels of linguistic units, such as words, sub-phrases and phrases, is helpful for measuring semantic similarity of an entire bilingual phrase. In this paper, we propose …...

AbstractWe address the problem of inducing word alignment for language pairs by developing an unsupervised model with the capability of getting applied to other generative alignment models. We approach the …...

AbstractHigh accuracy for automated translation and information retrieval calls for linguistic annotations at various language levels. The plethora of informal internet content sparked the demand for porting state-of-art natural language …...

We present a simple neural network for word alignment that builds source and tar- get word window representations to com- pute alignment scores for sentence pairs. To enable unsupervised training, …...

This paper describes our IR (Information Re- trieval) based method for SemEval 2016 task 1, Semantic Textual Similarity (STS). The main feature of our approach is to extend a conventional …...

AbstractWe describe our system (DT Team) submitted at SemEval-2017 Task 1, Semantic Textual Similarity (STS) challenge for English (Track 5). We developed three different models with various features including similarity …...

AbstractCurrent word alignment models do not distinguish between different types of alignment links. In this paper, we provide a new probabilistic model for word alignment where word alignments are associated …...

AbstractThe state of the art in machine translation (MT) is governed by neural approaches, which typically provide superior translation accuracy over statistical approaches. However, on the closely related task of …...

AbstractRecent progress on unsupervised cross-lingual embeddings in the bilingual setting has given the impetus to learning a shared embedding space for several languages. A popular framework to solve the latter …...

AbstractWe present a novel supervised word alignment method based on cross-language span prediction. We first formalize a word alignment problem as a collection of independent predictions from a token in …...

AbstractWord alignment was once a core unsupervised learning task in natural language processing because of its essential role in training statistical machine translation (MT) models. Although unnecessary for training neural …...

Word alignments identify translational correspondences between words in a parallel sentence pair and are used, for instance, to learn bilingual dictionaries, to train statistical machine trans- lation systems or to …...

AbstractMultilingual contextual embeddings, such as multilingual BERT and XLM-RoBERTa, have proved useful for many multi-lingual tasks. Previous work probed the cross-linguality of the representations indirectly using zero-shot transfer learning on …...

AbstractOne key principle for assessing textual similarity is measuring the degree of semantic overlap between texts by considering the word alignment. Such alignment-based approaches are both intuitive and interpretable; however, …...

AbstractWord alignment and machine translation are two closely related tasks. Neural translation models, such as RNN-based and Transformer models, employ a target-to-source attention mechanism which can provide rough word alignments, …...

AbstractThe advancement of the web and information technology has contributed to the rapid growth of digital libraries and automatic machine translation tools which easily translate texts from one language into …...

AbstractWord alignment, which aims to align translationally equivalent words between source and target sentences, plays an important role in many natural language processing tasks. Current unsupervised neural alignment methods focus …...

AbstractRecent work in simultaneous machine translation is often trained with conventional full sentence translation corpora, leading to either excessive latency or necessity to anticipate as-yet-unarrived words, when dealing with a …...

Autoregressive models have been widely used in unsupervised text style transfer. Despite their success, these models still suffer from the content preservation problem that they usu- ally ignore part of …...

AbstractWord alignment identify translational correspondences between words in a parallel sentence pair and are used and for example and to train statistical machine translation and learn bilingual dictionaries or to …...

AbstractWord alignment over parallel corpora has a wide variety of applications, including learning translation lexicons, cross-lingual transfer of language processing tools, and automatic evaluation or analysis of translation outputs. The …...

AbstractWord alignment which aims to extract lexicon translation equivalents between source and target sentences, serves as a fundamental tool for natural language processing. Recent studies in this area have yielded …...

AbstractAfter a period of decrease, interest in word alignments is increasing again for their usefulness in domains such as typological research, cross-lingual annotation projection and machine translation. Generally, alignment algorithms …...

AbstractWord alignment is essential for the downstream cross-lingual language understanding and generation tasks. Recently, the performance of the neural word alignment models has exceeded that of statistical models. However, they …...

AbstractWe demonstrate a simple yet effective approach to augmenting training data for multilingual named entity recognition using translations. The named entity spans from the original sentences are transferred to translations …...

AbstractMost existing word alignment methods rely on manual alignment datasets or parallel corpora, which limits their usefulness. Here, to mitigate the dependence on manual data, we broaden the source of …...