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检索条件"任意字段=Proceedings of the Third Workshop on Syntax and Structure in Statistical Translation"

共 111 条记录，以下是91-100 订阅

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Probabilistic synchronous tree-adjoining grammars for machine translation: The argument from bilingual dictionaries

Probabilistic synchronous tree-adjoining grammars for machin...

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： Shieber, Stuart M. School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States

We provide a conceptual basis for thinking of machine translation in terms of synchronous grammars in general, and probabilistic synchronous tree-adjoining grammars in particular. Evidence for the view is found in the structure of bilingual dictionaries of the last several millennia. © 2007 Association for Computational Linguistics

关键词： Machine translation

来源：评论

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Extraction phenomena in synchronous tag syntax and semantics

Extraction phenomena in synchronous tag syntax and semantics

引用

作者： Nesson, Rebecca Shieber, Stuart M. School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States

We present a proposal for the structure of noun phrases in Synchronous Tree-Adjoining Grammar (STAG) syntax and semantics that permits an elegant and uniform analysis of a variety of phenomena, including quantifier scope and extraction phenomena such as wh-questions with both moved and in-place wh-words, pied-piping, stranding of prepositions, and topicalization. The tight coupling between syntax and semantics enforced by the STAG helps to illuminate the critical relationships and filter out analyses that may be appealing for either syntax or semantics alone but do not allow for a meaningful relationship between them. © 2007 Association for Computational Linguistics

关键词： Semantics

来源：评论

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Three models for discriminative machine translation using global lexical selection and sentence reconstruction

Three models for discriminative machine translation using gl...

引用

作者： Venkatapathy, Sriram Bangalore, Srinivas Language Technologies Research Centre IIIT-Hyderabad Hyderabad500019 India AT&T Labs - Research Florham ParkNJ07932 United States

Machine translation of a source language sentence involves selecting appropriate target language words and ordering the selected words to form a well-formed target language sentence. Most of the previous work on statistical machine translation relies on (local) associations of target words/phrases with source words/phrases for lexical selection. In contrast, in this paper, we present a novel approach to lexical selection where the target words are associated with the entire source sentence (global) without the need for local associations. This technique is used by three models (Bag-of-words model, sequential model and hierarchical model) which predict the target language words given a source sentence and then order the words appropriately. We show that a hierarchical model performs best when compared to the other two models. © 2007 Association for Computational Linguistics

关键词： Machine translation

来源：评论

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Combining morphosyntactic enriched representation with n-best reranking in statistical translation

Combining morphosyntactic enriched representation with n-bes...

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作者： Bonneau-Maynard, H. Allauzen, A. Déchelotte, D. Schwenk, H. Spoken Language Processing Group LIMSI-CNRS BP 133 Orsay Cedex91403 France

The purpose of this work is to explore the integration of morphosyntactic information into the translation model itself, by enriching words with their morphosyntactic categories. We investigate word disambiguation using morphosyntactic categories, n-best hypotheses reranking, and the combination of both methods with word or morphosyntactic n-gram language model reranking. Experiments are carried out on the English-to-Spanish translation task. Using the morphosyntactic language model alone does not results in any improvement in performance. However, combining morphosyntactic word disambiguation with a word based 4-gram language model results in a relative improvement in the BLEU score of 2.3% on the development set and 1.9% on the test set. © 2007 Association for Computational Linguistics

关键词： translation (languages)

来源：评论

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Generation in machine translation from deep syntactic trees

Generation in machine translation from deep syntactic trees

引用

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

来源：评论

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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...

引用

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

来源：评论

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Inversion transduction grammar for joint phrasal translation modeling

Inversion transduction grammar for joint phrasal translation...

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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

来源：评论

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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

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Meta-structure transformation model for statistical machine translation 2

Meta-structure transformation model for statistical machine ...

引用

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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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...

引用

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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