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proceedings of SSST 2015: 9th workshop on syntax, semantics and structure in statistical translation - NAACL HLT 2015 / SIGMT / SIGLEX workshop

Proceedings of SSST 2015: 9th Workshop on Syntax, Semantics ...

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

ISBN: (纸本)9781941643419

The proceedings contain 12 papers. The topics discussed include: improving Chinese-English PropBank alignment;harmonizing word alignments and syntactic structures for extracting phrasal translation equivalents;non-projective dependency-based pre-reordering with recurrent neural network for machine translation;translating negation: induction, search and model errors;unsupervised false friend disambiguation using contextual word clusters and parallel word alignments;analyzing English-Spanish named-entity enhanced machine translation;and translation reranking using source phrase dependency features.

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translation reranking using source phrase dependency features 9

Translation reranking using source phrase dependency feature...

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

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

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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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proceedings of SSST 2014 - 8th workshop on syntax, semantics and structure in statistical translation

Proceedings of SSST 2014 - 8th Workshop on Syntax, Semantics...

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

ISBN: (纸本)9781937284961

The proceedings contain 18 papers. The topics discussed include: vector space models for phrase-based machine translation;bilingual Markov reordering labels for hierarchical SMT;better semantic frame based MT evaluation via inversion transduction grammars;rule-based syntactic preprocessing for syntax-based machine translation;applying HMEANT to English-Russian translations;reducing the impact of data sparsity in statistical machine translation;expanding the language model in a low-resource hybrid MT system;syntax and semantics in quality estimation of machine translation;and overcoming the curse of sentence length for neural machine translation using automatic segmentation.

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Analyzing English-Spanish Named-Entity enhanced Machine translation 9

Analyzing English-Spanish Named-Entity enhanced Machine Tran...

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

作者： Artetxe, Mikel Agirre, Eneko Alegria, Inaki Labaka, Gorka IXA NLP Group University of the Basque Country UPV/EHU

ISBN: (纸本)9781941643419

translation of named-entities (NEs) is an issue in SMT. In this paper we analyze the errors when translating NEs with a SMT system from English to Spanish. We train on Europarl and test on News Commentary, focusing on entities correctly recognized by an automatic NE recognition system. The automatic systems translate around 85% NEs correctly, leaving a small margin for improving performance. In addition, we implement a purpose-build NE translator and integrate it in the SMT system, yielding a small but significant improvement in BLEU score. Our analysis shows that, contrary to similar systems translating from Chinese to English, there was no improvement in NE translation, prompting further work. © 2015 Association for Computational Linguistics

关键词： Natural language processing systems

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Harmonizing word alignments and syntactic structures for extracting phrasal translation equivalents 9

Harmonizing word alignments and syntactic structures for ext...

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

作者： Deng, Dun Xue, Nianwen Guo, Shiman Computer Science Department Brandeis University 415 South Street WalthamMA02453 United States

ISBN: (纸本)9781941643419

Accurate identification of phrasal translation equivalents is critical to both phrase-based and syntax-based machine translation systems. We show that the extraction of many phrasal translation equivalents is made impossible by word alignments done without taking syntactic structures into consideration. To address the problem, we propose a new annotation scheme where word alignment and the alignment of non-terminal nodes (i.e., phrases) are done simultaneously to avoid conflicts between word alignments and syntactic structures. Relying on this new alignment approach, we construct a Hierarchically Aligned Chinese-English Parallel Treebank (HACEPT), and show that all phrasal translation equivalents can be automatically extracted based on the phrase alignments in HACEPT. © 2015 Association for Computational Linguistics

关键词： Alignment

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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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Word’s vector representations meet machine translation 8

Word’s vector representations meet machine translation

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

作者： Garcia, Eva Martínez España-Bonet, Cristina Tiedemann, Jörg Màrquez, Lluís TALP Research Center Univesitat Politècnica de Catalunya Spain Uppsala University Department of Linguistics and Philology Sweden Qatar Computing Research Institute Qatar Foundation Qatar

ISBN: (纸本)9781937284961

Distributed vector representations of words are useful in various NLP tasks. We briefly review the CBOW approach and propose a bilingual application of this architecture with the aim to improve consistency and coherence of Machine translation. The primary goal of the bilingual extension is to handle ambiguous words for which the different senses are conflated in the monolingual setup. © 2014 Association for Computational Linguistics

关键词： Machine translation

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How synchronous are adjuncts in translation data? 8

How synchronous are adjuncts in translation data?

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

作者： Arnoult, Sophie Sima’an, Khalil ILLC University of Amsterdam Netherlands

ISBN: (纸本)9781937284961

The argument-adjunct distinction is central to most syntactic and semantic theories. As optional elements that refine (the meaning of) a phrase, adjuncts are important for recursive, compositional accounts of syntax, semantics and translation. In formal accounts of machine translation, adjuncts are often treated as modifiers applying synchronously in source and target derivations. But how well can the assumption of synchronous adjunction explain translation equivalence in actual parallel data? In this paper we present the first empirical study of translation equivalence of adjuncts on a variety of French-English parallel corpora, while varying word alignments so we can gauge the effect of errors in them. We show that for proper measurement of the types of translation equivalence of adjuncts, we must work with non-contiguous, many-to-many relations, thereby amending the traditional Direct Correspondence Assumption. Our empirical results show that 70% of manually identified adjuncts have adjunct translation equivalents in training data, against roughly 50% for automatically identified adjuncts. © 2014 Association for Computational Linguistics

关键词： semantics

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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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