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translation reranking using source phrase dependency features 9

Translation reranking using source phrase dependency feature...

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 Dipartimento di Informatica Largo B. Pontecorvo 3 Pisa56127 Italy

We describe a N-best reranking model based on features that combine source-side dependency syntactical information and segmentation and alignment information. Specifically, we consider segmentation-aware"phrase d...

ISBN: (纸本)9781941643419

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

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

引用

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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Machine translation as tree labeling

Machine translation as tree labeling

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： 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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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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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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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

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Preference grammars and soft syntactic constraints for GHKM syntax-based statistical machine translation 8

Preference grammars and soft syntactic constraints for GHKM ...

引用

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

作者： Huck, Matthias Hoang, Hieu Koehn, Philipp School of Informatics University of Edinburgh 10 Crichton Street EdinburghEH8 9AB United Kingdom

ISBN: (纸本)9781937284961

In this work, we investigate the effectiveness of two techniques for a feature-based integration of syntactic information into GHKM string-to-tree statistical machine translation (Galley et al., 2004): (1.) Preference grammars on the target language side promote syntactic wellformedness during decoding while also allowing for derivations that are not linguistically motivated (as in hierarchical translation). (2.) Soft syntactic constraints augment the system with additional source-side syntax features while not modifying the set of string-to-tree translation rules or the baseline feature scores. We conduct experiments with a state-of-the-art setup on an English→German translation task. Our results suggest that preference grammars for GHKM translation are inferior to the plain target-syntactified model, whereas the enhancement with soft source syntactic constraints provides consistent gains. By employing soft source syntactic constraints with sparse features, we are able to achieve improvements of up to 0.7 points BLEU and 1.0 points TER. © 2014 Association for Computational Linguistics

关键词： Syntactics

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A study of translation rule classification for syntax-based statistical machine translation 3

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

引用

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： Jiang, Hongfei Li, Sheng Yang, Muyun Zhao, Tiejun School of Computer Science and Technology 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. © 2009 Association for Computational Linguistics

关键词： Computer aided language translation

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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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Inductive detection of language features via clustering minimal Pairs: Toward feature-rich grammars in machine translation 2

Inductive detection of language features via clustering mini...

引用

2nd workshop on syntax and structure in statistical translation, SSST 2008

作者： Clark, Jonathan H. Frederking, Robert Levin, Lori Language Technologies Institute Carnegie Mellon University PittsburghPA15213 United States

ISBN: (纸本)9781932432169

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. © 2008 Association for Computational Linguistics

关键词： Machine translation

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