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Combining morphosyntactic enriched representation with n-best reranking in statistical translation 07

Combining morphosyntactic enriched representation with n-bes...

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proceedings of the NAACL-HLT 2007/AMTA workshop on syntax and structure in statistical translation

作者： H. Bonneau-Maynard A. Allauzen D. Déchelotte H. Schwenk LIMSI-CNRS Orsay cedex France

The purpose of this work is to explore the integration of morphosyntactic information into the translation model itself, by enriching words with their morphosyntactic categories. We investigate word disambiguation using morphosyntactic categories, n-best hypotheses reranking, and the combination of both methods with word or morphosyntactic n-gram language model reranking. Experiments are carried out on the English-to-Spanish translation task. Using the morphosyntactic language model alone does not results in any improvement in performance. However, combining morphosyntactic word disambiguation with a word based 4-gram language model results in a relative improvement in the BLEU score of 2.3% on the development set and 1.9% on the test set.

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Inversion transduction grammar for joint phrasal translation modeling 07

Inversion transduction grammar for joint phrasal translation...

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proceedings of the NAACL-HLT 2007/AMTA workshop on syntax and structure in statistical translation

作者： Colin Cherry Dekang Lin University of Alberta Edmonton AB Canada Google Inc. Mountain View CA

We present a phrasal inversion transduction grammar as an alternative to joint phrasal translation models. This syntactic model is similar to its flat-string phrasal predecessors, but admits polynomial-time algorithms for Viterbi alignment and EM training. We demonstrate that the consistency constraints that allow flat phrasal models to scale also help ITG algorithms, producing an 80-times faster inside-outside algorithm. We also show that the phrasal translation tables produced by the ITG are superior to those of the flat joint phrasal model, producing up to a 2.5 point improvement in BLEU score. Finally, we explore, for the first time, the utility of a joint phrasal translation model as a word alignment method.

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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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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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Dependency-based automatic evaluation for machine translation 07

Dependency-based automatic evaluation for machine translatio...

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proceedings of the NAACL-HLT 2007/AMTA workshop on syntax and structure in statistical translation

作者： Karolina Owczarzak Josef van Genabith Andy Way Dublin City University Dublin Ireland

We present a novel method for evaluating the output of Machine translation (MT), based on comparing the dependency structures of the translation and reference rather than their surface string forms. Our method uses a treebank-based, widecoverage, probabilistic Lexical-Functional Grammar (LFG) parser to produce a set of structural dependencies for each translation-reference sentence pair, and then calculates the precision and recall for these dependencies. Our dependency-based evaluation, in contrast to most popular string-based evaluation metrics, will not unfairly penalize perfectly valid syntactic variations in the translation. In addition to allowing for legitimate syntactic differences, we use paraphrases in the evaluation process to account for lexical variation. In comparison with other metrics on 16,800 sentences of Chinese-English newswire text, our method reaches high correlation with human scores. An experiment with two translations of 4,000 sentences from Spanish-English Europarl shows that, in contrast to most other metrics, our method does not display a high bias towards statistical models of translation.

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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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proceedings of the Fifth workshop on syntax, Semantics and structure in statistical translation

作者： Qin Gao Stephan Vogel Carnegie Mellon University Pittsburgh PA

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.

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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...

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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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Generalizing local translation models 08

Generalizing local translation models

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proceedings of the Second workshop on syntax and structure in statistical translation

作者： Michael Subotin University of Maryland College Park MD

We investigate translation modeling based on exponential estimates which generalize essential components of standard translation models. In application to a hierarchical phrase-based system the simplest generalization allows its models of lexical selection and reordering to be conditioned on arbitrary attributes of the source sentence and its annotation. Viewing these estimates as approximations of sentence-level probabilities motivates further elaborations that seek to exploit general syntactic and morphological patterns. Dimensionality control with l1 regularizers makes it possible to negotiate the tradeoff between translation quality and decoding speed. Putting together and extending several recent advances in phrase-based translation we arrive at a flexible modeling framework that allows efficient leveraging of monolingual resources and tools. Experiments with features derived from the output of Chinese and Arabic parsers and an Arabic lemmatizer show significant improvements over a strong baseline.

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Three models for discriminative machine translation using global lexical selection and sentence reconstruction 07

Three models for discriminative machine translation using gl...

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proceedings of the NAACL-HLT 2007/AMTA workshop on syntax and structure in statistical translation

作者： Sriram Venkatapathy Srinivas Bangalore Language Technologies Research Centre IIIT-Hyderabad Hyderabad India AT&T Labs - Research Florham Park NJ

Machine translation of a source language sentence involves selecting appropriate target language words and ordering the selected words to form a well-formed target language sentence. Most of the previous work on statistical machine translation relies on (local) associations of target words/phrases with source words/phrases for lexical selection. In contrast, in this paper, we present a novel approach to lexical selection where the target words are associated with the entire source sentence (global) without the need for local associations. This technique is used by three models (Bag-of-words model, sequential model and hierarchical model) which predict the target language words given a source sentence and then order the words appropriately. We show that a hierarchical model performs best when compared to the other two models.

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Rich source-side context for statistical machine translation 08

Rich source-side context for statistical machine translation

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proceedings of the third workshop on statistical Machine translation

作者： Kevin Gimpel Noah A. Smith Carnegie Mellon University Pittsburgh PA

ISBN: (纸本)9781932432091

We explore the augmentation of statistical machine translation models with features of the context of each phrase to be translated. This work extends several existing threads of research in statistical MT, including the use of context in example-based machine translation (Carl and Way, 2003) and the incorporation of word sense disambiguation into a translation model (Chan et al., 2007). The context features we consider use surrounding words and part-of-speech tags, local syntactic structure, and other properties of the source language sentence to help predict each phrase's translation. Our approach requires very little computation beyond the standard phrase extraction algorithm and scales well to large data scenarios. We report significant improvements in automatic evaluation scores for Chinese-to-English and English-to-German translation, and also describe our entry in the WMT-08 shared task based on this approach.

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A scalable decoder for parsing-based machine translation with equivalent language model state maintenance 08

A scalable decoder for parsing-based machine translation wit...

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proceedings of the Second workshop on syntax and structure in statistical translation

作者： Zhifei Li Sanjeev Khudanpur Johns Hopkins University Baltimore MD

We describe a scalable decoder for parsing-based machine translation. The decoder is written in JAVA and implements all the essential algorithms described in Chiang (2007): chart-parsing, m-gram language model integration, beam- and cube-pruning, and unique k-best extraction. Additionally, parallel and distributed computing techniques are exploited to make it scalable. We also propose an algorithm to maintain equivalent language model states that exploits the back-off property of m-gram language models: instead of maintaining a separate state for each distinguished sequence of "state" words, we merge multiple states that can be made equivalent for language model probability calculations due to back-off. We demonstrate experimentally that our decoder is more than 30 times faster than a baseline decoder written in PYTHON. We propose to release our decoder as an open-source toolkit.

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