Chunk-lattices for verb reordering in Arabic–English statistical machine translation

Bisazza, Arianna; Pighin, Daniele; Federico, Marcello

doi:10.1007/s10590-011-9104-y

Cited by 8 publications

(17 citation statements)

References 23 publications

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“…SDSM and OSM have been proven optimal for language pairs where high distortion limits are required to capture long-range reordering phenomena (Durrani, Schmid, and Fraser 2011;Bisazza and Federico 2013b;…”

Section: Figurementioning

confidence: 99%

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

Bisazza

Federico

2016

Computational Linguistics

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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.

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Section: Figurementioning

confidence: 99%

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

Bisazza

Federico

2016

Computational Linguistics

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“…Green, Galley, and Manning (2010) additionally proposed discriminative distortion models to achieve better translation accuracy than the baseline phrase-based system for a distortion limit of 15 words. Bisazza and Federico (2013) recently proposed a novel method to dynamically select which longrange reorderings to consider during the hypothesis extension process in a phrasebased decoder and showed an improvement in a German-English task by increasing the distortion limit to 18.…”

Section: Phrase-based Smtmentioning

confidence: 99%

The Operation Sequence Model—Combining N-Gram-Based and Phrase-Based Statistical Machine Translation

Durrani

Schmid

Fraser

et al. 2015

Computational Linguistics

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In this article, we present a novel machine translation model, the Operation Sequence Model (OSM), which combines the benefits of phrase-based and N-gram-based statistical machine translation (SMT) and remedies their drawbacks. The model represents the translation process as a linear sequence of operations. The sequence includes not only translation operations but also reordering operations. As in N-gram-based SMT, the model is: (i) based on minimal translation units, (ii) takes both source and target information into account, (iii) does not make a phrasal independence assumption, and (iv) avoids the spurious phrasal segmentation problem. As in phrase-based SMT, the model (i) has the ability to memorize lexical reordering triggers, (ii) builds the search graph dynamically, and (iii) decodes with large translation units during search. The unique properties of the model are (i) its strong coupling of reordering and translation where translation and reordering decisions are conditioned on n previous translation and reordering decisions, and (ii) the ability to model local and long-range reorderings consistently. Using BLEU as a metric of translation accuracy, we found that our system performs significantly better than state-of-the-art phrase-based systems (Moses and Phrasal) and N-gram-based systems (Ncode) on standard translation tasks. We compare the reordering component of the OSM to the Moses lexical reordering model by integrating it into Moses. Our results show that OSM outperforms lexicalized reordering on all translation tasks. The translation quality is shown to be improved further by learning generalized representations with a POS-based OSM.

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“…Using language models for reordering is not something new (Feng et al, 2010), (Durrani et al, 2011), (Bisazza and Federico, 2013), but instead of using a more or less standard n-gram language model, we are going to base our model on recurrent neural network language models (Mikolov et al, 2010).…”

Section: Recurrent Neural Network Reordering Modelsmentioning

confidence: 99%

Non-projective Dependency-based Pre-Reordering with Recurrent Neural Network for Machine Translation

Barone

Attardi

2015

Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Confere

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The quality of statistical machine translation performed with phrase based approaches can be increased by permuting the words in the source sentences in an order which resembles that of the target language. We propose a class of recurrent neural models which exploit source-side dependency syntax features to reorder the words into a target-like order. We evaluate these models on the German-to-English and Italian-toEnglish language pairs, showing significant improvements over a phrasebased Moses baseline. We also compare with state of the art German-toEnglish pre-reordering rules, showing that our method obtains similar or better results.

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Chunk-lattices for verb reordering in Arabic–English statistical machine translation

Cited by 8 publications

References 23 publications

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

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

The Operation Sequence Model—Combining N-Gram-Based and Phrase-Based Statistical Machine Translation

Non-projective Dependency-based Pre-Reordering with Recurrent Neural Network for Machine Translation

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