A Survey of Word Reordering in Statistical Machine Translation: Computational Models and Language Phenomena

Word reordering is one of the most difficult aspects of statistical machine translation (SMT), and an important factor of its quality and efficiency. Despite the vast amount of research published to date, the interest of the community in this problem has not decreased, and no single method appears to be strongly dominant across language pairs. Instead, the choice of the optimal approach for a new translation task still seems to be mostly driven by empirical trials. To orientate the reader in this vast and complex research area, we present a comprehensive survey of word reordering viewed as a statistical modeling challenge and as a natural language phenomenon. The survey describes in detail how word reordering is modeled within different string-based and tree-based SMT frameworks and as a stand-alone task, including systematic overviews of the literature in advanced reordering modeling. We then question why some approaches are more successful than others in different language pairs. We argue that, besides measuring the amount of reordering, it is important to understand which kinds of reordering occur in a given language pair. To this end, we conduct a qualitative analysis of word reordering phenomena in a diverse sample of language pairs, based on a large collection of linguistic knowledge. Empirical results in the SMT literature are shown to support the hypothesis that a few linguistic facts can be very useful to anticipate the reordering characteristics of a language pair and to select the SMT framework that best suits them.Comment: 44 pages, to appear in Computational Linguistic

The impact of source-side syntactic reordering on hierarchical phrase-based SMT

Syntactic reordering has been demonstrated to be helpful and effective for handling different word orders between source and target languages in SMT. However, in terms of hierarchial PB-SMT (HPB), does the syntactic reordering still has a significant impact on its performance? This paper introduces a reordering approach which explores the { (DE) grammatical structure in Chinese. We employ the Stanford DE classifier to recognise the DE structures in both training and test sentences of Chinese, and then perform word reordering to make the Chinese sentences better match the word order of English. The annotated and reordered training data and test data are applied to a re-implemented HPB system and the impact of the DE construction is examined. The experiments are conducted on the NIST 2008 evaluation data and experimental results show that the BLEU and METEOR scores are significantly improved by 1.83/8.91 and 1.17/2.73 absolute/ relative points respectively

Bayesian reordering model with feature selection

In phrase-based statistical machine translation systems, variation in grammatical structures between source and target languages can cause large movements of phrases. Modeling such movements is crucial in achieving translations of long sentences that appear natural in the target language. We explore generative learning approach to phrase reordering in Arabic to English. Formulating the reordering problem as a classification problem and using naive Bayes with feature selection, we achieve an improvement in the BLEU score over a lexicalized reordering model. The proposed model is compact, fast and scalable to a large corpus

A discriminative latent variable-based "DE" classifier for Chinese–English SMT

Syntactic reordering on the source-side is an effective way of handling word order differences. The (DE) construction is a flexible and ubiquitous syntactic structure in Chinese which is a major source of error in translation quality. In this paper, we propose a new classifier model — discriminative latent variable model (DPLVM) — to classify the DE construction to improve the accuracy of the classification and hence the translation quality. We also propose a new feature which can automatically learn the reordering rules to a certain extent. The experimental results show that the MT systems using the data reordered by our proposed model outperform the baseline systems by 6.42% and 3.08% relative points in terms of the BLEU score on PB-SMT and hierarchical phrase-based MT respectively. In addition, we analyse the impact of DE annotation on word alignment and on the SMT phrase table

How much hybridisation does machine translation need?

This is the peer reviewed version of the following article: [Costa-jussà, M. R. (2015), How much hybridization does machine translation Need?. J Assn Inf Sci Tec, 66: 2160–2165. doi:10.1002/asi.23517], which has been published in final form at [10.1002/asi.23517]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.Rule-based and corpus-based machine translation (MT)have coexisted for more than 20 years. Recently, bound-aries between the two paradigms have narrowed andhybrid approaches are gaining interest from bothacademia and businesses. However, since hybridapproaches involve the multidisciplinary interaction oflinguists, computer scientists, engineers, and informa-tion specialists, understandably a number of issuesexist.While statistical methods currently dominate researchwork in MT, most commercial MT systems are techni-cally hybrid systems. The research community shouldinvestigate the bene¿ts and questions surrounding thehybridization of MT systems more actively. This paperdiscusses various issues related to hybrid MT includingits origins, architectures, achievements, and frustra-tions experienced in the community. It can be said thatboth rule-based and corpus- based MT systems havebene¿ted from hybridization when effectively integrated.In fact, many of the current rule/corpus-based MTapproaches are already hybridized since they do includestatistics/rules at some point.Peer ReviewedPostprint (author's final draft

Fine-grained human evaluation of neural versus phrase-based machine translation

We compare three approaches to statistical machine translation (pure phrase-based, factored phrase-based and neural) by performing a fine-grained manual evaluation via error annotation of the systems' outputs. The error types in our annotation are compliant with the multidimensional quality metrics (MQM), and the annotation is performed by two annotators. Inter-annotator agreement is high for such a task, and results show that the best performing system (neural) reduces the errors produced by the worst system (phrase-based) by 54%.Comment: 12 pages, 2 figures, The Prague Bulletin of Mathematical Linguistic

Towards String-to-Tree Neural Machine Translation

We present a simple method to incorporate syntactic information about the target language in a neural machine translation system by translating into linearized, lexicalized constituency trees. An experiment on the WMT16 German-English news translation task resulted in an improved BLEU score when compared to a syntax-agnostic NMT baseline trained on the same dataset. An analysis of the translations from the syntax-aware system shows that it performs more reordering during translation in comparison to the baseline. A small-scale human evaluation also showed an advantage to the syntax-aware system.Comment: Accepted as a short paper in ACL 201

A detailed analysis of phrase-based and syntax-based machine translation: the search for systematic differences

This paper describes a range of automatic and manual comparisons of phrase-based and syntax-based statistical machine translation methods applied to English-German and English-French translation of user-generated content. The syntax-based methods underperform the phrase-based models and the relaxation of syntactic constraints to broaden translation rule coverage means that these models do not necessarily generate output which is more grammatical than the output produced by the phrase-based models. Although the systems generate different output and can potentially be fruitfully combined, the lack of systematic difference between these models makes the combination task more challenging