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Proceedings of ssst 2008 - 2nd workshop on syntax and structure in statistical translation

Proceedings of SSST 2008 - 2nd Workshop on Syntax and Struct...

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2nd workshop on syntax and structure in statistical translation, ssst 2008

ISBN: (纸本)9781932432169

The proceedings contain 11 papers. The topics discussed include: imposing constraints from the source tree on ITG constraints for SMT;a scalable decoder for parsing-based machine translation with equivalent language model state maintenance;prior derivation models for formally syntax-based translation using linguistically syntactic parsing and tree kernels;generalizing local translation models;a rule-driven dynamic programming decoder for statistical MT;syntactic reordering integrated with phrase-based SMT;experiments in discriminating phrase-based translations on the basis of syntactic coupling features;multiple reorderings in phrase-based machine translation;and improving word alignment using syntactic dependencies.

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Prior derivation models for formally syntax-based translation using linguistically syntactic parsing and tree kernels 2

Prior derivation models for formally syntax-based translatio...

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2nd workshop on syntax and structure in statistical translation, ssst 2008

作者： Zhou, Bowen Xiang, Bing Zhu, Xiaodan Gao, Yuqing IBM T. J. Watson Research Center Yorktown HeightsNY United States Dept. of Computer Science University of Toronto Canada

ISBN: (纸本)9781932432169

This paper presents an improved formally syntax-based SMT model, which is enriched by linguistically syntactic knowledge obtained from statistical constituent parsers. We propose a linguistically-motivated prior derivation model to score hypothesis derivations on top of the baseline model during the translation decoding. Moreover, we devise a fast training algorithm to achieve such improved models based on tree kernel methods. Experiments on an English-to-Chinese task demonstrate that our proposed models outperformed the baseline formally syntax-based models, while both of them achieved significant improvements over a state-of-the-art phrase-based SMT system. © 2008 Association for Computational Linguistics

关键词： Forestry

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syntax-driven learning of sub-sentential translation equivalents and translation rules from parsed parallel corpora 2

Syntax-driven learning of sub-sentential translation equival...

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2nd workshop on syntax and structure in statistical translation, ssst 2008

作者： Lavie, Alon Parlikar, Alok Ambati, Vamshi Language Technologies Institute Carnegie Mellon University PittsburghPA15213 United States

ISBN: (纸本)9781932432169

We describe a multi-step process for automatically learning reliable sub-sentential syntactic phrases that are translation equivalents of each other and syntactic translation rules between two languages. The input to the process is a corpus of parallel sentences, word-aligned and annotated with phrase-structure parse trees. We first apply a newly developed algorithm for aligning parse-tree nodes between the two parallel trees. Next, we extract all aligned sub-sentential syntactic constituents from the parallel sentences, and create a syntax-based phrase-table. Finally, we treat the node alignments as tree decomposition points and extract from the corpus all possible synchronous parallel tree fragments. These are then converted into synchronous context-free rules. We describe the approach and analyze its application to Chinese-English parallel data. © 2008 Association for Computational Linguistics

关键词： Syntactics

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Inductive detection of language features via clustering minimal Pairs: Toward feature-rich grammars in machine translation 2

Inductive detection of language features via clustering mini...

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2nd workshop on syntax and structure in statistical translation, ssst 2008

作者： Clark, Jonathan H. Frederking, Robert Levin, Lori Language Technologies Institute Carnegie Mellon University PittsburghPA15213 United States

ISBN: (纸本)9781932432169

syntax-based Machine translation systems have recently become a focus of research with much hope that they will outperform traditional Phrase-Based statistical Machine translation (PBSMT). Toward this goal, we present a method for analyzing the morphosyntactic content of language from an Elicitation Corpus such as the one included in the LDC's upcoming LCTL language packs. The presented method discovers a mapping between morphemes and linguistically relevant features. By providing this tool that can augment structure-based MT models with these rich features, we believe the discriminative power of current models can be improved. We conclude by outlining how the resulting output can then be used in inducing a morphosyntactically feature-rich grammar for AVENUE, a modern syntax-based MT system. © 2008 Association for Computational Linguistics

关键词： Machine translation

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Improving word alignment using syntactic dependencies 2

Improving word alignment using syntactic dependencies

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2nd workshop on syntax and structure in statistical translation, ssst 2008

作者： Ma, Yanjun Ozdowska, Sylwia Sun, Yanli Way, Andy School of Computing Dublin City University Dublin Ireland School of Applied Language and Intercultural Studies Dublin City University Dublin Ireland

ISBN: (纸本)9781932432169

We introduce a word alignment framework that facilitates the incorporation of syntax encoded in bilingual dependency tree pairs. Our model consists of two sub-models: an anchor word alignment model which aims to find a set of high-precision anchor links and a syntax-enhanced word alignment model which focuses on aligning the remaining words relying on dependency information invoked by the acquired anchor links. We show that our syntax-enhanced word alignment approach leads to a 10.32% and 5.57% relative decrease in alignment error rate compared to a generative word alignment model and a syntax-proof discriminative word alignment model respectively. Furthermore, our approach is evaluated extrinsically using a phrase-based statistical machine translation system. The results show that SMT systems based on our word alignment approach tend to generate shorter outputs. Without length penalty, using our word alignments yields statistically significant improvement in Chinese-English machine translation in comparison with the baseline word alignment. © 2008 Association for Computational Linguistics

关键词： Machine translation

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Generalizing local translation models 2

Generalizing local translation models

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2nd workshop on syntax and structure in statistical translation, ssst 2008

作者： Subotin, Michael Laboratory for Computational Linguistics and Information Processing Department of Linguistics University of Maryland College ParkMD20742 United States

ISBN: (纸本)9781932432169

We investigate translation modeling based on exponential estimates which generalize essential components of standard translation models. In application to a hierarchical phrase-based system the simplest generalization allows its models of lexical selection and reordering to be conditioned on arbitrary attributes of the source sentence and its annotation. Viewing these estimates as approximations of sentence-level probabilities motivates further elaborations that seek to exploit general syntactic and morphological patterns. Dimensionality control with1 regularizers makes it possible to negotiate the tradeoff between translation quality and decoding speed. Putting together and extending several recent advances in phrase-based translation we arrive at a flexible modeling framework that allows efficient leveraging of monolingual resources and tools. Experiments with features derived from the output of Chinese and Arabic parsers and an Arabic lemmatizer show significant improvements over a strong baseline. © 2008 Association for Computational Linguistics

关键词： Syntactics

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Multiple reorderings in phrase-based machine translation 2

Multiple reorderings in phrase-based machine translation

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2nd workshop on syntax and structure in statistical translation, ssst 2008

作者： Ge, Niyu Ittycheriah, Abe Papineni, Kishore IBM T.J.Watson Research 1101 Kitchawan Rd Yorktown HeightsNY10598 United States Yahoo! Research 45 West 18th St. New YorkNY10011 United States

ISBN: (纸本)9781932432169

This paper presents a method to integrate multiple reordering strategies in phrase-based statistical machine translation. Recently there has been much research effort in reordering problems in machine translation. State-of-the-art decoders incorporate sophisticated local reordering strategies, but there is little research on a unified approach to incorporate various kinds of reordering methods. We present a phrase-based decoder which easily allows multiple reordering schemes. We show how to use this framework to perform distance-based reordering and HIERO-style (Chiang 2005) hierarchical reordering. We also present two novel syntax-based reordering methods, one built on part-of-speech tags and the other based on parse trees. We will give experimental results using these relatively easy to implement methods on standard tests. © 2008 Association for Computational Linguistics

关键词： Machine translation

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A scalable decoder for parsing-based machine translation with equivalent language model state maintenance 2

A scalable decoder for parsing-based machine translation wit...

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2nd workshop on syntax and structure in statistical translation, ssst 2008

作者： Li, Zhifei Khudanpur, Sanjeev Department of Computer Science and Center for Language and Speech Processing Johns Hopkins University BaltimoreMD21218 United States

ISBN: (纸本)9781932432169

We describe a scalable decoder for parsing-based machine translation. The decoder is written in JAVA and implements all the essential algorithms described in Chiang (2007): chart-parsing, m-gram language model integration, beam- and cube-pruning, and unique k-best extraction. Additionally, parallel and distributed computing techniques are exploited to make it scalable. We also propose an algorithm to maintain equivalent language model states that exploits the back-off property of m-gram language models: instead of maintaining a separate state for each distinguished sequence of "state" words, we merge multiple states that can be made equivalent for language model probability calculations due to back-off. We demonstrate experimentally that our decoder is more than 30 times faster than a baseline decoder written in PYTHON. We propose to release our decoder as an open-source toolkit. © 2008 Association for Computational Linguistics

关键词： Computational linguistics

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Imposing constraints from the source tree on ITG constraints for SMT 2

Imposing constraints from the source tree on ITG constraints...

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2nd workshop on syntax and structure in statistical translation, ssst 2008

作者： Yamamoto, Hirofumi Okuma, Hideo Sumita, Eiichiro National Institute of Information and Communications Technology 2-2-2 Hikaridai Seika-cho Soraku-gun Kyoto Japan ATR Spoken Language Communication Research Labs Kinki University School of Sience and Engineering Department of Information

ISBN: (纸本)9781932432169

In current statistical machine translation (SMT), erroneous word reordering is one of the most serious problems. To resolve this problem, many word-reordering constraint techniques have been proposed. The inversion transduction grammar (ITG) is one of these constraints. In ITG constraints, target-side word order is obtained by rotating nodes of the source-side binary tree. In these node rotations, the source binary tree instance is not considered. Therefore, stronger constraints for word reordering can be obtained by imposing further constraints derived from the source tree on the ITG constraints. For example, for the source word sequence (abcd), ITG constraints allow a total of twenty-two target word orderings. However, when the source binary tree instance ((a b) (c d)) is given, our proposed"imposing source tree on ITG" (IST-ITG) constraints allow only eight word orderings. The reduction in the number of word-order permutations by our proposed stronger constraints efficiently suppresses erroneous word orderings. In our experiments with IST-ITG using the NIST MT08 English-to-Chinese translation track's data, the proposed method resulted in a 1.8-points improvement in character BLEU-4 (35.2 to 37.0) and a 6.2% lower CER (74.1 to 67.9%) compared with our baseline condition. © 2008 Association for Computational Linguistics

关键词： Binary trees

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Meta-structure transformation model for statistical machine translation 2

Meta-structure transformation model for statistical machine ...

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2nd workshop on statistical Machine translation, WMT 2007 at the 45th Annual Meeting of the Association of Computational Linguistics, ACL 2007

作者： Sun, Jiadong Zhao, Tiejun Liang, Huashen MOE-MS Key Lab of National Language Processing and Speech Harbin Institute of Technology No. 92 West Da-zhi Street Heilongjiang Harbin150001 China

We propose a novel syntax-based model for statistical machine translation in which meta-structure (MS) and meta-structure sequence (SMS) of a parse tree are defined. In this framework, a parse tree is decomposed into SMS to deal with the structure divergence and the alignment can be reconstructed at different levels of recombination of MS (RM). RM pairs extracted can perform the mapping between the substructures across languages. As a result, we have got not only the translation for the target language, but an SMS of its parse tree at the same time. Experiments with BLEU metric show that the model significantly outperforms Pharaoh, a state-art-the-art phrase-based system. © 2007 Association for Computational Linguistics

关键词： Computer aided language translation

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