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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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Proceedings of the ssst 2013 - 7th workshop on syntax, semantics and structure in statistical translation, SIGMT/SIGLEX workshop - 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2013

Proceedings of the SSST 2013 - 7th Workshop on Syntax, Seman...

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7th workshop on syntax, semantics and structure in statistical translation, ssst 2013

ISBN: (纸本)9781937284473

the proceedings contain 8 papers. the topics discussed include: a semantic evaluation of machine translation lexical choice;taste of two different flavors: which Manipuri script works better for English-Manipuri language pair SMT systems?;hierarchical alignment decomposition labels for hiero grammar rules;a performance study of cube pruning for large-scale hierarchical machine translation;combining word reordering methods on different linguistic abstraction levels for statistical machine translation;a formal characterization of parsing word alignments by synchronous grammars with empirical evidence to the ITG hypothesis;and synchronous linear context-free rewriting systems for machine translation.

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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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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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Expanding the language model in a low-resource hybrid MT system 8

Expanding the language model in a low-resource hybrid MT sys...

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

作者： Tambouratzis, George Sofianopoulos, Sokratis Vassiliou, Marina ILSP Athena R.C

ISBN: (纸本)9781937284961

the present article investigates the fusion of different language models to improve translation accuracy. A hybrid MT system, recently-developed in the European Commission-funded PRESEMT project that combines example-based MT and statistical MT principles is used as a starting point. In this article, the syntactically-defined phrasal language models (NPs, VPs etc.) used by this MT system are supplemented by n-gram language models to improve translation accuracy. For specific structural patterns, n-gram statistics are consulted to determine whether the pattern instantiations are corroborated. Experiments indicate improvements in translation accuracy. © 2014 Association for Computational Linguistics

关键词： translation (languages)

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

Preference grammars and soft syntactic constraints for GHKM ...

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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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Overcoming the curse of sentence length for neural machine translation using automatic segmentation 8

Overcoming the curse of sentence length for neural machine t...

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

作者： Pouget-Abadie, Jean Bahdanau, Dzmitry van Merriënboer, Bart Cho, Kyunghyun Bengio, Yoshua Ecole Polytechnique France Jacobs University Bremen Germany Université de Montréal Canada

ISBN: (纸本)9781937284961

the authors of (Cho et al., 2014a) have shown that the recently introduced neural network translation systems suffer from a significant drop in translation quality when translating long sentences, unlike existing phrase-based translation systems. In this paper, we propose a way to address this issue by automatically segmenting an input sentence into phrases that can be easily translated by the neural network translation model. Once each segment has been independently translated by the neural machine translation model, the translated clauses are concatenated to form a final translation. Empirical results show a significant improvement in translation quality for long sentences. © 2014 Association for Computational Linguistics

关键词： Neural machine translation

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On the properties of neural machine translation: Encoder–decoder approaches 8

On the properties of neural machine translation: Encoder–de...

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

作者： Cho, Kyunghyun van Merriënboer, Bart Bahdanau, Dzmitry Bengio, Yoshua Université de Montréal Canada Jacobs University Bremen Germany

ISBN: (纸本)9781937284961

Neural machine translation is a relatively new approach to statistical machine translation based purely on neural networks. the neural machine translation models often consist of an encoder and a decoder. the encoder extracts a fixed-length representation from a variable-length input sentence, and the decoder generates a correct translation from this representation. In this paper, we focus on analyzing the properties of the neural machine translation using two models;RNN Encoder–Decoder and a newly proposed gated recursive convolutional neural network. We show that the neural machine translation performs relatively well on short sentences without unknown words, but its performance degrades rapidly as the length of the sentence and the number of unknown words increase. Furthermore, we find that the proposed gated recursive convolutional network learns a grammatical structure of a sentence automatically. © 2014 Association for Computational Linguistics

关键词： Neural machine translation

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