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检索条件"任意字段=Proceedings of the Fifth Workshop on Syntax, Semantics and Structure in Statistical Translation"

共 56 条记录，以下是41-50 订阅

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A general-purpose rule extractor for SCFG-based machine translation 5

A general-purpose rule extractor for SCFG-based machine tran...

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

ISBN: (纸本)9781932432992

We present a rule extractor for SCFG-based MT that generalizes many of the contraints present in existing SCFG extraction algorithms. Our method’s increased rule coverage comes from allowing multiple alignments, virtual nodes, and multiple tree decompositions in the extraction process. At decoding time, we improve automatic metric scores by significantly increasing the number of phrase pairs that match a given test set, while our experiments with hierarchical grammar filtering indicate that more intelligent filtering schemes will also provide a key to future gains. © 2011 Association for Computational Linguistics

关键词： Extraction

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structured vs. Flat semantic role representations for machine translation evaluation 5

Structured vs. Flat semantic role representations for machin...

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： Lo, Chi-Kiu Wu, Dekai HKUST Human Language Technology Center Dept. of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9781932432992

We argue that failing to capture the degree of contribution of each semantic frame in a sentence explains puzzling results in recent work on the MEANT family of semantic MT evaluation metrics, which have disturbingly indicated that dissociating semantic roles and fillers from their predicates actually improves correlation with human adequacy judgments even though, intuitively, properly segregating event frames should more accurately reflect the preservation of meaning. Our analysis finds that both properly structured and flattened representations fail to adequately account for the contribution of each semantic frame to the overall sentence. We then show that the correlation of HMEANT, the human variant of MEANT, can be greatly improved by introducing a simple length-based weighting scheme that approximates the degree of contribution of each semantic frame to the overall sentence. The new results also show that, without flattening the structure of semantic frames, weighting the degree of each frame’s contribution gives HMEANT higher correlations than the previously best-performing flattened model, as well as HTER. © 2011 Association for Computational Linguistics

关键词： semantics

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

Improving reordering for statistical machine translation wit...

引用

proceedings of the fifth workshop on syntax, semantics and structure in statistical translation

作者： Bing Xiang Niyu Ge Abraham Ittycheriah IBM T. J. Watson Research Center Yorktown Heights NY

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.

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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...

引用

proceedings of the fifth workshop on syntax, semantics and structure in statistical translation

作者： John McCrae Mauricio Espinoza Elena Montiel-Ponsoda Guadalupe Aguado-de-Cea Philipp Cimiano Universität Bielefeld Bielefeld Germany Universidad de Cuenca Cuenca Ecuador 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.

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A dependency based statistical translation model 5

A dependency based statistical translation model

引用

proceedings of the fifth workshop on syntax, semantics and structure in statistical translation

作者： Giuseppe Attardi Atanas Chanev Antonio Valerio Miceli Barone Universitá di Pisa

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.

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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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proceedings of the fifth workshop on syntax, semantics and structure in statistical translation

作者： Greg Hanneman Alon Lavie Carnegie Mellon University Pittsburgh PA

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.

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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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proceedings of the fifth workshop on syntax, semantics and structure in statistical translation

作者： Daniele Pighin Lluís Màrquez Universitat Politècnica de Catalunya

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.

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A general-purpose rule extractor for SCFG-based machine translation 5

A general-purpose rule extractor for SCFG-based machine tran...

引用

proceedings of the fifth workshop on syntax, semantics and structure in statistical translation

作者： Greg Hanneman Michelle Burroughs Alon Lavie Carnegie Mellon University Pittsburgh PA

ISBN: (纸本)9781932432992

We present a rule extractor for SCFG-based MT that generalizes many of the contraints present in existing SCFG extraction algorithms. Our method's increased rule coverage comes from allowing multiple alignments, virtual nodes, and multiple tree decompositions in the extraction process. At decoding time, we improve automatic metric scores by significantly increasing the number of phrase pairs that match a given test set, while our experiments with hierarchical grammar filtering indicate that more intelligent filtering schemes will also provide a key to future gains.

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A semantic feature for statistical machine translation 5

A semantic feature for statistical machine translation

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proceedings of the fifth workshop on syntax, semantics and structure in statistical translation

作者： Rafael E. Banchs Marta R. Costa-jussà Institute for Infocomm Research Singapore Barcelona Media Innovation Centre planta Barcelona

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.

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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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proceedings of the fifth workshop on syntax, semantics and structure in statistical translation

作者： Hwidong Na Jong-Hyeok Lee Pohang University of Science and Technology (POSTECH) Pohang Republic of Korea

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.

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