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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...

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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 phrase-based translation via word alignments from stochastic inversion transduction grammars 3

Improving phrase-based translation via word alignments from ...

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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： Saers, Markus Wu, Dekai Dept. of Linguistics and Philology Uppsala University Sweden Human Language Technology Center Dept. of Computer Science and Engineering HKUST Hong Kong

ISBN: (纸本)9781932432398

We argue that learning word alignments through a compositionally-structured, joint process yields higher phrase-based translation accuracy than the conventional heuristic of intersecting conditional models. Flawed word alignments can lead to flawed phrase translations that damage translation accuracy. Yet the IBM word alignments usually used today are known to be flawed, in large part because IBM models (1) model reordering by allowing unrestricted movement of words, rather than constrained movement of compositional units, and therefore must (2) attempt to compensate via directed, asymmetric distortion and fertility models. The conventional heuristics for attempting to recover from the resulting alignment errors involve estimating two directed models in opposite directions and then intersecting their alignments - to make up for the fact that, in reality, word alignment is an inherently joint relation. A natural alternative is provided by Inversion Transduction Grammars, which estimate the joint word alignment relation directly, eliminating the need for any of the conventional heuristics. We show that this alignment ultimately produces superior translation accuracy on BLEU, NIST, and METEOR metrics over three distinct language pairs. © 2009 Association for Computational Linguistics

关键词： Alignment

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Machine translation as tree labeling 07

Machine translation as tree labeling

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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

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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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Automatic projection of semantic structures: An application to pairwise translation ranking 5

Automatic projection of semantic structures: An application ...

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5th workshop on syntax, Semantics and structure in statistical translation, SSST 2011 at the Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL hlt 2011

作者： Pighin, Daniele Màrquez, Lluís TALP Research Center Universitat Politècnica de Catalunya Spain

ISBN: (纸本)9781932432992

We present a model for the inclusion of semantic role annotations in the framework of confidence estimation for machine translation. The model has several interesting properties, most notably: 1) it only requires a linguistic processor on the (generally well-formed) source side of the translation;2) it does not directly rely on properties of the translation model (hence, it can be applied beyond phrase-based systems). These features make it potentially appealing for system ranking, translation re-ranking and user feedback evaluation. Preliminary experiments in pairwise hypothesis ranking on five confidence estimation benchmarks show that the model has the potential to capture salient aspects of translation quality. © 2011 Association for Computational Linguistics

关键词： Benchmarking

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CCG Syntactic Reordering Models for Phrase-based Machine translation 7

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

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7th workshop on statistical Machine translation, WMT 2012, immediately following the Conference of the North-American Chapter of the Association for Computational Linguistics - Human Language Technologies, naacl hlt 2012

作者： Mehay, Dennis N. Brew, Chris Ohio State University ColumbusOH United States Educational Testing Service PrincetonNJ United States

ISBN: (纸本)9781937284206

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 ungrammatical translations. syntax, or sentence structure, could provide guidance to phrasebased 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 nonconstituent 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. ©2012 Association for Computational Linguistics.

关键词： Syntactics

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Generation in machine translation from deep syntactic trees 07

Generation in machine translation from deep syntactic trees

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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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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...

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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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Inversion transduction grammar for joint phrasal translation modeling 07

Inversion transduction grammar for joint phrasal translation...

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

作者： Colin Cherry Dekang Lin University of Alberta Edmonton AB Canada Google Inc. Mountain View CA

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.

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