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A Semantic Evaluation of Machine translation Lexical Choice 7

A Semantic Evaluation of Machine Translation Lexical Choice

引用

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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Reducing the impact of data sparsity in statistical machine translation 8

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

引用

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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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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A dependency based statistical translation model 5

A dependency based statistical translation model

引用

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： Attardi, Giuseppe Chanev, Atanas Miceli Barone, Antonio Valerio Università di Pisa Dipartimento di Informatica Italy

ISBN: (纸本)9781932432992

We present a translation model based on dependency trees. The model adopts a tree-to-string approach and extends Phrase-Based translation (PBT) by using the dependency tree of the source sentence for selecting translation options and for reordering them. Decoding is done by translating each node in the tree and combining its translations with those of its head in alternative orders with respect to its siblings. Reordering of the siblings exploits a heuristic based on the syntactic information from the parse tree which is learned from the corpus. The decoder uses the same phrase tables produced by a PBT system for looking up translations of single words or of partial sub-trees. A mathematical model is presented and experimental results are discussed. © 2011 Association for Computational Linguistics

关键词： Forestry

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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...

引用

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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An evaluation and possible improvement path for current SMT behavior on ambiguous nouns 5

An evaluation and possible improvement path for current SMT ...

引用

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： Lefever, Els Hoste, Véronique LT3 Language and Translation Technology Team University College Ghent Groot-Brittanniëlaan 45 Gent9000 Belgium Gent9000 Belgium Dept. of Linguistics Ghent University Blandijnberg 2 Gent9000 Belgium

ISBN: (纸本)9781932432992

Mistranslation of an ambiguous word can have a large impact on the understandability of a given sentence. In this article, we describe a thorough evaluation of the translation quality of ambiguous nouns in three different setups. We compared two statistical Machine translation systems and one dedicated Word Sense Disambiguation (WSD) system. Our WSD system incorporates multilingual information and is independent from external lexical resources. Word senses are derived automatically from word alignments on a parallel corpus. We show that the two WSD classifiers that were built for these experiments (English–French and English–Dutch) outperform the SMT system that was trained on the same corpus. This opens perspectives for the integration of our multilingual WSD module in a statistical Machine translation framework, in order to improve the automated translation of ambiguous words, and by consequence make the translation output more understandable. © 2011 Association for Computational Linguistics

关键词： Computer aided language translation

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How synchronous are adjuncts in translation data? 8

How synchronous are adjuncts in translation data?

引用

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

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

引用

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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Decoding with syntactic and non-syntactic phrases in a syntax-based machine translation system 3

Decoding with syntactic and non-syntactic phrases in a synta...

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： Hanneman, Greg Lavie, Alon Language Technologies Institute Carnegie Mellon University PittsburghPA15213 United States

ISBN: (纸本)9781932432398

A key concern in building syntax-based machine translation systems is how to improve coverage by incorporating more traditional phrase-based SMT phrase pairs that do not correspond to syntactic constituents. At the same time, it is desirable to include as much syntactic information in the system as possible in order to carry out linguistically motivated reordering, for example. We apply an extended and modified version of the approach of Tinsley et al. (2007), extracting syntax-based phrase pairs from a large parallel parsed corpus, combining them with PBSMT phrases, and performing joint decoding in a syntax-based MT framework without loss of translation quality. This effectively addresses the low coverage of purely syntactic MT without discarding syntactic information. Further, we show the potential for improved translation results with the inclusion of a syntactic grammar. We also introduce a new syntax-prioritized technique for combining syntactic and non-syntactic phrases that reduces overall phrase table size and decoding time by 61%, with only a minimal drop in automatic translation metric scores. © 2009 Association for Computational Linguistics

关键词： Syntactics

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Improving word alignment using syntactic dependencies 2

Improving word alignment using syntactic dependencies

引用

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

作者： Ma, Yanjun Ozdowska, Sylwia Sun, Yanli Way, Andy School of Computing Dublin City University Dublin Ireland School of Applied Language and Intercultural Studies Dublin City University Dublin Ireland

ISBN: (纸本)9781932432169

We introduce a word alignment framework that facilitates the incorporation of syntax encoded in bilingual dependency tree pairs. Our model consists of two sub-models: an anchor word alignment model which aims to find a set of high-precision anchor links and a syntax-enhanced word alignment model which focuses on aligning the remaining words relying on dependency information invoked by the acquired anchor links. We show that our syntax-enhanced word alignment approach leads to a 10.32% and 5.57% relative decrease in alignment error rate compared to a generative word alignment model and a syntax-proof discriminative word alignment model respectively. Furthermore, our approach is evaluated extrinsically using a phrase-based statistical machine translation system. The results show that SMT systems based on our word alignment approach tend to generate shorter outputs. Without length penalty, using our word alignments yields statistically significant improvement in Chinese-English machine translation in comparison with the baseline word alignment. © 2008 Association for Computational Linguistics

关键词： Machine translation

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