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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11-20 订阅

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Non-projective Dependency-based Pre-Reordering with Recurrent Neural Network for Machine translation 9

Non-projective Dependency-based Pre-Reordering with Recurren...

引用

9th workshop on syntax, Semantics and structure in statistical translation, SSST 2015 at the NAACL HLT 2015

作者： Miceli-Barone, Antonio Valerio Attardi, Giuseppe Università di Pisa Largo B. Pontecorvo 3 Pisa56127 Italy

ISBN: (纸本)9781941643419

The quality of statistical machine translation performed with phrase based approaches can be increased by permuting the words in the source sentences in an order which resembles that of the target language. We propose a class of recurrent neural models which exploit source-side dependency syntax features to reorder the words into a target-like order. We evaluate these models on the German-to-English language pair, showing significant improvements over a phrase-based Moses baseline, obtaining a quality similar or superior to that of hand-coded syntactical reordering rules. © 2015 Association for Computational Linguistics

关键词： Recurrent neural networks

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syntax-oriented evaluation measures for machine translation output 4

Syntax-oriented evaluation measures for machine translation ...

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4th workshop on statistical Machine translation, WMT 2009, immediately preceding the 12th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2009

作者： Popovíc, Maja Ney, Hermann RWTH Aachen University Aachen Germany

We explored novel automatic evaluation measures for machine translation output oriented to the syntactic structure of the sentence: the BLEU score on the detailed Part-of-Speech (POS) tags as well as the precision, recall and F-measure obtained on POS n-grams. We also introduced Fmeasure based on both word and POS ngrams. Correlations between the new metrics and human judgments were calculated on the data of the first, second and third shared task of the statistical Machine translation workshop. Machine translation outputs in four different European languages were taken into account: English, Spanish, French and German. The results show that the new measures correlate very well with the human judgements and that they are competitive with the widely used BLEU, METEOR and TER metrics. © 2009 Association for Computational Linguistics.

关键词： Machine translation

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Machine translation as tree labeling

Machine translation as tree labeling

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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： Hopkins, Mark Kuhn, Jonas Department of Linguistics 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-and-bound 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. © 2007 Association for Computational Linguistics

关键词： Forestry

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Rule-based syntactic preprocessing for syntax-based machine translation 8

Rule-based syntactic preprocessing for syntax-based machine ...

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8th workshop on syntax, Semantics and structure in statistical translation, SSST 2014

作者： Hatakoshi, Yuto Neubig, Graham Sakti, Sakriani Toda, Tomoki Nakamura, Satoshi Nara Institute of Science and Technology Graduate School of Information Science Takayama Nara Ikoma630-0192 Japan

ISBN: (纸本)9781937284961

Several preprocessing techniques using syntactic information and linguistically motivated rules have been proposed to improve the quality of phrase-based machine translation (PBMT) output. On the other hand, there has been little work on similar techniques in the context of other translation formalisms such as syntax-based SMT. In this paper, we examine whether the sort of rule-based syntactic preprocessing approaches that have proved beneficial for PBMT can contribute to syntax-based SMT. Specifically, we tailor a highly successful preprocessing method for English-Japanese PBMT to syntax-based SMT, and find that while the gains achievable are smaller than those for PBMT, significant improvements in accuracy can be realized. © 2014 Association for Computational Linguistics

关键词： Syntactics

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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...

引用

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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Extraction phenomena in synchronous tag syntax and semantics

Extraction phenomena in synchronous tag syntax and semantics

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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

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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...

引用

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

Probabilistic synchronous tree-adjoining grammars for machin...

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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

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Reducing the impact of data sparsity in statistical machine translation 8

Reducing the impact of data sparsity in statistical machine ...

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8th workshop on syntax, Semantics and structure in statistical translation, SSST 2014

作者： Singla, Karan Sachdeva, Kunal Yadav, Diksha Bangalore, Srinivas Sharma, Dipti Misra LTRC IIIT Hyderabad India AT&T Labs-Research

ISBN: (纸本)9781937284961

Morphologically rich languages generally require large amounts of parallel data to adequately estimate parameters in a statistical Machine translation(SMT) system. However, it is time consuming and expensive to create large collections of parallel data. In this paper, we explore two strategies for circumventing sparsity caused by lack of large parallel corpora. First, we explore the use of distributed representations in an Recurrent Neural Network based language model with different morphological features and second, we explore the use of lexical resources such as WordNet to overcome sparsity of content words. © 2014 Association for Computational Linguistics

关键词： Recurrent neural networks

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A Semantic Evaluation of Machine translation Lexical Choice 7

A Semantic Evaluation of Machine Translation Lexical Choice

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7th workshop on syntax, Semantics and structure in statistical translation, SSST 2013

作者： Carpuat, Marine National Research Council 1200 Montreal Rd OttawaONK1A 0R6 Canada

ISBN: (纸本)9781937284473

While automatic metrics of translation quality are invaluable for machine translation research, deeper understanding of translation errors require more focused evaluations designed to target specific aspects of translation quality. We show that Word Sense Disambiguation (WSD) can be used to evaluate the quality of machine translation lexical choice, by applying a standard phrase-based SMT system on the SemEval2010 Cross-Lingual WSD task. This case study reveals that the SMT system does not perform as well as a WSD system trained on the exact same parallel data, and that local context models based on source phrases and target n-grams are much weaker representations of context than the simple templates used by the WSD system. ©2013 Association for Computational Linguistics.

关键词： Machine translation

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