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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 - proceedings of the workshop

5th Workshop on Syntax, Semantics and Structure in Statistic...

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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

ISBN: (纸本)9781932432992

The proceedings contain 15 papers. The topics discussed include: automatic projection of semantic structures: an application to pairwise translation ranking;structured vs. flat semantic role representations for machine translation evaluation;semantic mapping using automatic word alignment and semantic role labeling;incorporating source-language paraphrases into phrase-based SMT with confusion networks;multi-word unit dependency forest-based translation rule extraction;an evaluation and possible improvement path for current SMT behavior on ambiguous nouns;and improving reordering for statistical machine translation with smoothed priors and syntactic features.

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Combining statistical and semantic approaches to the translation of ontologies and taxonomies 5

Combining statistical and semantic approaches to the transla...

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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： McCrae, John Espinoza, Mauricio Montiel-Ponsoda, Elena Aguado-De-Cea, Guadalupe Cimiano, Philipp AG Semantic Computing Universität Bielefeld Bielefeld Germany Universidad de Cuenca Cuenca Ecuador Ontology Engineering Group Universidad Politécnica de Madrid Madrid Spain

ISBN: (纸本)9781932432992

Ontologies and taxonomies are widely used to organize concepts providing the basis for activities such as indexing, and as background knowledge for NLP tasks. As such, translation of these resources would prove useful to adapt these systems to new languages. However, we show that the nature of these resources is significantly different from the "free-text" paradigm used to train most statistical machine translation systems. In particular, we see significant differences in the linguistic nature of these resources and such resources have rich additional semantics. We demonstrate that as a result of these linguistic differences, standard SMT methods, in particular evaluation metrics, can produce poor performance. We then look to the task of leveraging these semantics for translation, which we approach in three ways: by adapting the translation system to the domain of the resource;by examining if semantics can help to predict the syntactic structure used in translation;and by evaluating if we can use existing translated taxonomies to disambiguate translations. We present some early results from these experiments, which shed light on the degree of success we may have with each approach. © 2011 Association for Computational Linguistics

关键词： semantics

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A dependency based statistical translation model 5

A dependency based statistical translation model

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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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Utilizing target-side semantic role labels to assist hierarchical phrase-based machine translation 5

Utilizing target-side semantic role labels to assist hierarc...

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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： Gao, Qin Vogel, Stephan Language Technologies Institute Carnegie Mellon University 5000 Forbes Avenue PittsburghPA15213 United States

ISBN: (纸本)9781932432992

In this paper we present a novel approach of utilizing Semantic Role Labeling (SRL) information to improve Hierarchical Phrase-based Machine translation. We propose an algorithm to extract SRL-aware Synchronous Context-Free Grammar (SCFG) rules. Conventional Hiero-style SCFG rules will also be extracted in the same framework. Special conversion rules are applied to ensure that when SRL-aware SCFG rules are used in derivation, the decoder only generates hypotheses with complete semantic structures. We perform machine translation experiments using 9 different Chinese-English test-sets. Our approach achieved an average BLEU score improvement of 0.49 as well as 1.21 point reduction in TER. © 2011 Association for Computational Linguistics

关键词： semantics

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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 ...

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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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Multi-word unit dependency forest-based translation rule extraction 5

Multi-word unit dependency forest-based translation rule ext...

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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： Na, Hwidong Lee, Jong-Hyeok San 31 Hyoja Dong Pohang790-784 Korea Republic of

ISBN: (纸本)9781932432992

translation requires non-isomorphic transformation from the source to the target. However, non-isomorphism can be reduced by learning multi-word units (MWUs). We present a novel way of representating sentence structure based on MWUs, which are not necessarily continuous word sequences. Our proposed method builds a simpler structure of MWUs than words using words as vertices of a dependency structure. Unlike previous studies, we collect many alternative structures in a packed forest. As an application of our proposed method, we extract translation rules in form of a source MWU-forest to the target string, and verify the rule coverage empirically. As a consequence, we improve the rule coverage compare to a previous work, while retaining the linear asymptotic complexity. © 2011 Association for Computational Linguistics

关键词： Forestry

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Improving reordering for statistical machine translation with smoothed priors and syntactic features 5

Improving reordering for statistical machine translation wit...

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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： Xiang, Bing Ge, Niyu Ittycheriah, Abraham IBM T. J. Watson Research Center Yorktown HeightsNY10598 United States

ISBN: (纸本)9781932432992

In this paper we propose several novel approaches to improve phrase reordering for statistical machine translation in the framework of maximum-entropy-based modeling. A smoothed prior probability is introduced to take into account the distortion effect in the priors. In addition to that we propose multiple novel distortion features based on syntactic parsing. A new metric is also introduced to measure the effect of distortion in the translation hypotheses. We show that both smoothed priors and syntax-based features help to significantly improve the reordering and hence the translation performance on a large-scale Chinese-to-English machine translation task. © 2011 Association for Computational Linguistics

关键词： Machine translation

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A semantic feature for statistical machine translation 5

A semantic feature for statistical machine translation

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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： Banchs, Rafael E. Costa-Jussà, Marta R. Institute for Infocomm Research 1 Fusionopolis Way 21-01 Singapore138632 Singapore Barcelona Media Innovation Centre Av. Diagonal 177 planta 9 Barcelona08018 Spain

ISBN: (纸本)9781932432992

A semantic feature for statistical machine translation, based on Latent Semantic Indexing, is proposed and evaluated. The objective of the proposed feature is to account for the degree of similarity between a given input sentence and each individual sentence in the training dataset. This similarity is computed in a reduced vector-space constructed by means of the Latent Semantic Indexing decomposition. The computed similarity values are used as an additional feature in the log-linear model combination approach to statistical machine translation. In our implementation, the proposed feature is dynamically adjusted for each translation unit in the translation table according to the current input sentence to be translated. This model aims at favoring those translation units that were extracted from training sentences that are semantically related to the current input sentence being translated. Experimental results on a Spanish-to-English translation task on the Bible corpus demonstrate a significant improvement on translation quality with respect to a baseline system. © 2011 Association for Computational Linguistics

关键词： Vector spaces

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Automatic category label coarsening for syntax-based machine translation 5

Automatic category label coarsening for syntax-based machine...

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

ISBN: (纸本)9781932432992

We consider SCFG-based MT systems that get syntactic category labels from parsing both the source and target sides of parallel training data. The resulting joint nonterminals often lead to needlessly large label sets that are not optimized for an MT scenario. This paper presents a method of iteratively coarsening a label set for a particular language pair and training corpus. We apply this label collapsing on Chinese–English and French–English grammars, obtaining test-set improvements of up to 2.8 BLEU, 5.2 TER, and 0.9 METEOR on Chinese–English translation. An analysis of label collapsing’s effect on the grammar and the decoding process is also given. © 2011 Association for Computational Linguistics

关键词： Coarsening

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Automatic projection of semantic structures: An application to pairwise translation ranking 5

Automatic projection of semantic structures: An application ...

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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： Pighin, Daniele Màrquez, Lluís TALP Research Center Universitat Politècnica de Catalunya Spain

ISBN: (纸本)9781932432992

We present a model for the inclusion of semantic role annotations in the framework of confidence estimation for machine translation. The model has several interesting properties, most notably: 1) it only requires a linguistic processor on the (generally well-formed) source side of the translation;2) it does not directly rely on properties of the translation model (hence, it can be applied beyond phrase-based systems). These features make it potentially appealing for system ranking, translation re-ranking and user feedback evaluation. Preliminary experiments in pairwise hypothesis ranking on five confidence estimation benchmarks show that the model has the potential to capture salient aspects of translation quality. © 2011 Association for Computational Linguistics

关键词： Benchmarking

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