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WSD for n-best reranking and local language modeling in SMT 12

WSD for n-best reranking and local language modeling in SMT

引用

workshop on syntax and structure in statistical translation

作者： Marianna Apidianaki Guillaume Wisniewski Artem Sokolov Aurelien Max Francois Yvon LIMSI-CNRS BP 133 F-91403 Orsay Cedex France Univ. Paris Sud BP 133 F-91403 Orsay Cedex France

ISBN: (纸本)9781627483452

We integrate semantic information at two stages of the translation process of a state-of-the-art SMT system. A Word Sense Disambiguation (WSD) classifier produces a probability distribution over the translation candidates of source words which is exploited in two ways. First, the probabilities serve to rerank a list of n-best translations produced by the system. Second, the WSD predictions are used to build a supplementary language model for each sentence, aimed to favor translations that seem more adequate in this specific sentential context. Both approaches lead to significant improvements in translation performance, highlighting the usefulness of source side disambiguation for SMT.

关键词： modelling languages Surface mount technology translation Process Probability distribution translations

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The influence of argument structure on semantic role assignment

The influence of argument structure on semantic role assignm...

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2004 Conference on Empirical Methods in Natural Language Processing, EMNLP 2004

作者： Padó, Sebastian Boleda, Gemma SALSA Dept. of Computational Linguistics Saarland University Saarbrücken Germany GLiCom Dept. of Translation and Interpreting Pompeu Fabra University Barcelona Spain

We present a data and error analysis for semantic role labelling. In a first experiment, we build a generic statistical model for semantic role assignment in the FrameNet paradigm and show that there is a high variance in performance across frames. The main hypothesis of our paper is that this variance is to a large extent a result of differences in the underlying argument structure of the predicates in different frames. In a second experiment, we show that frame uniformity, which measures argument structure variation, correlates well with the performance figures, effectively explaining the variance. © 2005 Association for Computational Linguistics

关键词： Semantics

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CCG Syntactic Reordering Models for Phrase-based Machine translation 12

CCG Syntactic Reordering Models for Phrase-based Machine Tra...

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workshop on statistical Machine translation

作者： Dennis N. Mehay Chris Brew The Ohio State University Columbus OH USA Educational Testing Service Princeton NJ USA

ISBN: (纸本)9781622765928

statistical phrase-based machine translation requires no linguistic information beyond word-aligned parallel corpora (Zens et al., 2002; Koehn et al., 2003). Unfortunately, this linguistic agnosticism often produces un-grammatical translations. syntax, or sentence structure, could provide guidance to phrase-based systems, but the "non-constituent" word strings that phrase-based decoders manipulate complicate the use of most recursive syntactic tools. We address these issues by using Combinatory Categorial Grammar, or CCG, (Steedman, 2000), which has a much more flexible notion of constituency, thereby providing more labels for putative non-constituent multiword translation phrases. Using CCG parse charts, we train a syntactic analogue of a lexicalized reordering model by labelling phrase table entries with multiword labels and demonstrate significant improvements in translating between Urdu and English, two language pairs with divergent sentence structure.

关键词： machine translation syntax Sentence structure combined cycle power generation Urdu linguistic data Guidance

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Application of Clause Alignment for statistical Machine translation 12

Application of Clause Alignment for Statistical Machine Tran...

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workshop on syntax and structure in statistical translation

作者： Svetla Koeva Borislav Rizov Ivelina Stoyanova Svetlozara Leseva Rositsa Dekova Angel Genov Ekaterina Tarpomanova Tsvetana Dimitrova Hristina Kukova Department of Computational Linguistics Institute for Bulgarian Language Bulgarian Academy of Sciences Sofia 1113 Bulgaria

ISBN: (纸本)9781627483452

The paper presents a new resource light flexible method for clause alignment which combines the Gale-Church algorithm with internally collected textual information. The method does not resort to any pre-developed linguistic resources which makes it very appropriate for resource light clause alignment. We experiment with a combination of the method with the original Gale-Church algorithm (1993) applied for clause alignment. The performance of this flexible method, as it will be referred to hereafter, is measured over a specially designed test corpus. The clause alignment is explored as means to provide improved training data for the purposes of statistical Machine translation (SMT). A series of experiments with Moses demonstrate ways to modify the parallel resource and effects on translation quality: (1) baseline training with a Bulgarian-English parallel corpus aligned at sentence level; (2) training based on parallel clause pairs; (3) training with clause reordering, where clauses in each source language (SL) sentence are reordered according to order of the clauses in the target language (TL) sentence. Evaluation is based on BLEU score and shows small improvement when using the clause aligned corpus.

关键词： machine translation Alignment reduced mass

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Unsupervised vs. supervised weight estimation for semantic MT evaluation metrics 12

Unsupervised vs. supervised weight estimation for semantic M...

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workshop on syntax and structure in statistical translation

作者： Chi-kiu Lo Dekai Wu HKUST Human Language Technology Center Department of Computer Science and Engineering Hong Kong University of Science and Technology

ISBN: (纸本)9781627483452

We present an unsupervised approach to estimate the appropriate degree of contribution of each semantic role type for semantic translation evaluation, yielding a semantic MT evaluation metric whose correlation with human adequacy judgments is comparable to that of recent supervised approaches but without the high cost of a human-ranked training corpus. Our new unsupervised estimation approach is motivated by an analysis showing that the weights learned from supervised training are distributed in a similar fashion to the relative frequencies of the semantic roles. Empirical results show that even without a training corpus of human adequacy rankings against which to optimize correlation, using instead our relative frequency weighting scheme to approximate the importance of each semantic role type leads to a semantic MT evaluation metric that correlates comparable with human adequacy judgments to previous metrics that require far more expensive human rankings of adequacy over a training corpus. As a result, the cost of semantic MT evaluation is greatly reduced.

关键词： Semantics weight estimation ADEQUACY machine translation MT Metronidazole Comparable

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A study of translation rule classification for syntax-based statistical machine translation 09

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

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Proceedings of the Third workshop on syntax and structure in statistical translation

作者： Hongfei Jiang Sheng Li Muyun Yang Tiejun Zhao 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.

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A walk on the other side: adding statistical components to a transfer-based translation system 07

A walk on the other side: adding statistical components to a...

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Proceedings of the NAACL-HLT 2007/AMTA workshop on syntax and structure in statistical translation

作者： Ariadna Font Llitjós Stephan Vogel Carnegie Mellon University Pittsburgh PA

This paper seeks to complement the current trend of adding more structure to statistical Machine translation systems, by exploring the opposite direction: adding statistical components to a Transfer-Based MT system. Initial results on the BTEC data show significant improvement according to three automatic evaluation metrics (BLEU, NIST and METEOR).

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Improving reordering for statistical machine translation with smoothed priors and syntactic features 5

Improving reordering for statistical machine translation wit...

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Proceedings of the Fifth workshop on syntax, Semantics and structure in statistical translation

作者： Bing Xiang Niyu Ge Abraham Ittycheriah IBM T. J. Watson Research Center Yorktown Heights NY

ISBN: (纸本)9781932432992

In this paper we propose several novel approaches to improve phrase reordering for statistical machine translation in the framework of maximum-entropy-based modeling. A smoothed prior probability is introduced to take into account the distortion effect in the priors. In addition to that we propose multiple novel distortion features based on syntactic parsing. A new metric is also introduced to measure the effect of distortion in the translation hypotheses. We show that both smoothed priors and syntax-based features help to significantly improve the reordering and hence the translation performance on a large-scale Chinese-to-English machine translation task.

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Inductive detection of language features via clustering minimal pairs: toward feature-rich grammars in machine translation 08

Inductive detection of language features via clustering mini...

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Proceedings of the Second workshop on syntax and structure in statistical translation

作者： Jonathan H. Clark Robert Frederking Lori Levin Carnegie Mellon University Pittsburgh PA

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.

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Prior derivation models for formally syntax-based translation using linguistically syntactic parsing and tree kernels 08

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

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Proceedings of the Second workshop on syntax and structure in statistical translation

作者： Bowen Zhou Xiaodan Zhu Bing Xiang Yuqing Gao IBM T. J. Watson Research Center Yorktown Heights NY University of Toronto

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.

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