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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

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： 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

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Generating case markers in machine translation

Generating case markers in machine translation

引用

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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Probabilistic synchronous tree-adjoining grammars for machine translation: the argument from bilingual dictionaries 07

Probabilistic synchronous tree-adjoining grammars for machin...

引用

proceedings of the naacl-hlt 2007/amta workshop on syntax and structure in statistical translation

作者： Stuart M. Shieber Harvard University Cambridge MA

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...

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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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Machine translation as tree labeling 07

Machine translation as tree labeling

引用

proceedings of the naacl-hlt 2007/amta workshop on syntax and structure in statistical translation

作者： Mark Hopkins Jonas Kuhn University of Potsdam Germany

We present the main ideas behind a new syntax-based machine translation system, based on reducing the machine translation task to a tree-labeling task. This tree labeling is further reduced to a sequence of decisions (of four varieties), which can be discriminatively trained. The optimal tree labeling (i.e. translation) is then found through a simple depth-first branch-andbound search. An early system founded on these ideas has been shown to be competitive with Pharaoh when both are trained on a small subsection of the Europarl corpus.

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Extraction phenomena in synchronous TAG syntax and semantics 07

Extraction phenomena in synchronous TAG syntax and semantics

引用

proceedings of the naacl-hlt 2007/amta workshop on syntax and structure in statistical translation

作者： Rebecca Nesson Stuart M. Shieber Harvard University Cambridge MA

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.

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Chunk-level reordering of source language sentences with automatically learned rules for statistical machine translation 07

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

引用

proceedings of the naacl-hlt 2007/amta workshop on syntax and structure in statistical translation

作者： Yuqi Zhang Richard Zens Hermann Ney RWTH Aachen University Aachen 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.

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Generation in machine translation from deep syntactic trees 07

Generation in machine translation from deep syntactic trees

引用

proceedings of the naacl-hlt 2007/amta workshop on syntax and structure in statistical translation

作者： Keith Hall Petr Němec Johns Hopkins University Baltimore MD 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 subphrase reordering. We show by way of empirical results that this model is effective in constructing acceptable English sentences given impoverished trees.

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Combining morphosyntactic enriched representation with n-best reranking in statistical translation 07

Combining morphosyntactic enriched representation with n-bes...

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proceedings of the naacl-hlt 2007/amta workshop on syntax and structure in statistical translation

作者： H. Bonneau-Maynard A. Allauzen D. Déchelotte H. Schwenk LIMSI-CNRS Orsay cedex 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.

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