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Inversion transduction grammar for joint phrasal translation modeling

Inversion transduction grammar for joint phrasal translation...

引用

2007 amta workshop on syntax and structure in statistical translation, SSST 2007 at the 2007 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, naacl-hlt 2007

作者： Cherry, Colin Lin, Dekang Department of Computing Science University of Alberta EdmontonABT6G 2E8 Canada Google Inc. 1600 Amphitheatre Parkway Mountain ViewCA9403 United States

We present a phrasal inversion transduction grammar as an alternative to joint phrasal translation models. This syntactic model is similar to its flat-string phrasal predecessors, but admits polynomial-time algorithms for Viterbi alignment and EM training. We demonstrate that the consistency constraints that allow flat phrasal models to scale also help ITG algorithms, producing an 80-times faster inside-outside algorithm. We also show that the phrasal translation tables produced by the ITG are superior to those of the flat joint phrasal model, producing up to a 2.5 point improvement in BLEU score. Finally, we explore, for the first time, the utility of a joint phrasal translation model as a word alignment method. © 2007 Association for Computational Linguistics

关键词： Bacteriophages

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Generation in machine translation from deep syntactic trees

Generation in machine translation from deep syntactic trees

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作者： Hall, Keith Němec, Petr Center for Language and Speech Processing Johns Hopkins University BaltimoreMD21218 United States Institute of Formal and Applied Linguistics Charles University Prague Czech Republic

In this paper we explore a generative model for recovering surface syntax and strings from deep-syntactic tree structures. Deep analysis has been proposed for a number of language and speech processing tasks, such as machine translation and paraphrasing of speech transcripts. In an effort to validate one such formalism of deep syntax, the Praguian Tectogrammatical Representation (TR), we present a model of synthesis for English which generates surface-syntactic trees as well as strings. We propose a generative model for function word insertion (prepositions, definite/indefinite articles, etc.) and sub-phrase reordering. We show by way of empirical results that this model is effective in constructing acceptable English sentences given impoverished trees. © 2007 Association for Computational Linguistics

关键词： Syntactics

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Chunk-level reordering of source language sentences with automatically learned rules for statistical machine translation

Chunk-level reordering of source language sentences with aut...

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作者： Zhang, Yuqi Zens, Richard Ney, Hermann Human Language Technology and Pattern Recognition Lehrstuhl für Informatik 6 Computer Science Department RWTH Aachen University AachenD-52056 Germany

In this paper, we describe a source-side reordering method based on syntactic chunks for phrase-based statistical machine translation. First, we shallow parse the source language sentences. Then, reordering rules are automatically learned from source-side chunks and word alignments. During translation, the rules are used to generate a reordering lattice for each sentence. Experimental results are reported for a Chinese-to-English task, showing an improvement of 0.5%-1.8% BLEU score absolute on various test sets and better computational efficiency than reordering during decoding. The experiments also show that the reordering at the chunk-level performs better than at the POS-level. © 2007 Association for Computational Linguistics

关键词： Computational efficiency

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Dependency-based automatic evaluation for machine translation

Dependency-based automatic evaluation for machine translatio...

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作者： Owczarzak, Karolina van Genabith, Josef Way, Andy National Centre for Language Technology School of Computing Dublin City University Dublin 9 Ireland

We present a novel method for evaluating the output of Machine translation (MT), based on comparing the dependency structures of the translation and reference rather than their surface string forms. Our method uses a treebank-based, wide-coverage, probabilistic Lexical-Functional Grammar (LFG) parser to produce a set of structural dependencies for each translation-reference sentence pair, and then calculates the precision and recall for these dependencies. Our dependency-based evaluation, in contrast to most popular string-based evaluation metrics, will not unfairly penalize perfectly valid syntactic variations in the translation. In addition to allowing for legitimate syntactic differences, we use paraphrases in the evaluation process to account for lexical variation. In comparison with other metrics on 16,800 sentences of Chinese-English newswire text, our method reaches high correlation with human scores. An experiment with two translations of 4,000 sentences from Spanish-English Europarl shows that, in contrast to most other metrics, our method does not display a high bias towards statistical models of translation. © 2007 Association for Computational Linguistics

关键词： Machine translation

来源：评论

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translation reranking using source phrase dependency features 9

Translation reranking using source phrase dependency feature...

引用

9th workshop on syntax, Semantics and structure in statistical translation, SSST 2015 at the naacl hlt 2015

作者： Miceli-Barone, Antonio Valerio Dipartimento di Informatica Largo B. Pontecorvo 3 Pisa56127 Italy

We describe a N-best reranking model based on features that combine source-side dependency syntactical information and segmentation and alignment information. Specifically, we consider segmentation-aware"phrase d...

ISBN: (纸本)9781941643419

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Non-projective Dependency-based Pre-Reordering with Recurrent Neural Network for Machine translation 9

Non-projective Dependency-based Pre-Reordering with Recurren...

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9th workshop on syntax, Semantics and structure in statistical translation, SSST 2015 at the naacl hlt 2015

作者： Miceli-Barone, Antonio Valerio Attardi, Giuseppe Università di Pisa Largo B. Pontecorvo 3 Pisa56127 Italy

ISBN: (纸本)9781941643419

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 language pair, showing significant improvements over a phrase-based Moses baseline, obtaining a quality similar or superior to that of hand-coded syntactical reordering rules. © 2015 Association for Computational Linguistics

关键词： Recurrent neural networks

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Generating case markers in machine translation

Generating case markers in machine translation

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Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics, naacl hlt 2007

作者： Toutanova, Kristina Suzuki, Hisami Microsoft Research One Microsoft Way Redmond WA 98052 United States

We study the use of rich syntax-based statistical models for generating grammatical case for the purpose of machine translation from a language which does not indicate case explicitly (English) to a language with a rich system of surface case markers (Japanese). We propose an extension of n-best re-ranking as a method of integrating such models into a statistical MT system and show that this method substantially outperforms standard n-best re-ranking. Our best performing model achieves a statistically significant improvement over the baseline MT system according to the BLEU metric. Human evaluation also confirms the results. © 2007 Association for Computational Linguistics.

关键词： Machine translation

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A study of translation rule classification for syntax-based statistical machine translation 3

A study of translation rule classification for syntax-based ...

引用

3rd workshop on syntax and structure in statistical translation, SSST 2009 at the 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, naacl-hlt 2009

作者： Jiang, Hongfei Li, Sheng Yang, Muyun Zhao, Tiejun School of Computer Science and Technology Harbin Institute of Technology

ISBN: (纸本)9781932432398

Recently, numerous statistical machine translation models which can utilize various kinds of translation rules are proposed. In these models, not only the conventional syntactic rules but also the non-syntactic rules can be applied. Even the pure phrase rules are includes in some of these models. Although the better performances are reported over the conventional phrase model and syntax model, the mixture of diversified rules still leaves much room for study. In this paper, we present a refined rule classification system. Based on this classification system, the rules are classified according to different standards, such as lexicalization level and generalization. Especially, we refresh the concepts of the structure reordering rules and the discontiguous phrase rules. This novel classification system may supports the SMT research community with some helpful references. © 2009 Association for Computational Linguistics

关键词： Computer aided language translation

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Decoding with syntactic and non-syntactic phrases in a syntax-based machine translation system 3

Decoding with syntactic and non-syntactic phrases in a synta...

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作者： Hanneman, Greg Lavie, Alon Language Technologies Institute Carnegie Mellon University PittsburghPA15213 United States

ISBN: (纸本)9781932432398

A key concern in building syntax-based machine translation systems is how to improve coverage by incorporating more traditional phrase-based SMT phrase pairs that do not correspond to syntactic constituents. At the same time, it is desirable to include as much syntactic information in the system as possible in order to carry out linguistically motivated reordering, for example. We apply an extended and modified version of the approach of Tinsley et al. (2007), extracting syntax-based phrase pairs from a large parallel parsed corpus, combining them with PBSMT phrases, and performing joint decoding in a syntax-based MT framework without loss of translation quality. This effectively addresses the low coverage of purely syntactic MT without discarding syntactic information. Further, we show the potential for improved translation results with the inclusion of a syntactic grammar. We also introduce a new syntax-prioritized technique for combining syntactic and non-syntactic phrases that reduces overall phrase table size and decoding time by 61%, with only a minimal drop in automatic translation metric scores. © 2009 Association for Computational Linguistics

关键词： Syntactics

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Analyzing English-Spanish Named-Entity enhanced Machine translation 9

Analyzing English-Spanish Named-Entity enhanced Machine Tran...

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9th workshop on syntax, Semantics and structure in statistical translation, SSST 2015 at the naacl hlt 2015

作者： Artetxe, Mikel Agirre, Eneko Alegria, Inaki Labaka, Gorka IXA NLP Group University of the Basque Country UPV/EHU

ISBN: (纸本)9781941643419

translation of named-entities (NEs) is an issue in SMT. In this paper we analyze the errors when translating NEs with a SMT system from English to Spanish. We train on Europarl and test on News Commentary, focusing on entities correctly recognized by an automatic NE recognition system. The automatic systems translate around 85% NEs correctly, leaving a small margin for improving performance. In addition, we implement a purpose-build NE translator and integrate it in the SMT system, yielding a small but significant improvement in BLEU score. Our analysis shows that, contrary to similar systems translating from Chinese to English, there was no improvement in NE translation, prompting further work. © 2015 Association for Computational Linguistics

关键词： Natural language processing systems

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