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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21-30 订阅

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Zero pronoun resolution can improve the quality of J-E translation 6

Zero pronoun resolution can improve the quality of J-E trans...

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6th workshop on syntax, semantics and structure in statistical translation, SSST 2012 at the 50th Annual Meeting of the Association for Computational Linguistics, ACT 2012

作者： Taira, Hirotoshi Sudoh, Katsuhito Nagata, Masaaki NTT Communication Science Laboratories 2-4 Hikaridai Seika-cho Kyoto Keihanna Science City619-0237 Japan

ISBN: (纸本)9781937284381

In Japanese, particularly, spoken Japanese, subjective, objective and possessive cases are very often omitted. Such Japanese sentences are often translated by Japanese-English statistical machine translation to the English sentence whose subjective, objective and possessive cases are omitted, and it causes to decrease the quality of translation. We performed experiments of J-E phrase based translation using Japanese sentence, whose omitted pronouns are complemented by human. We introduced 'antecedent F-measure' as a score for measuring quality of the translated English. As a result, we found that it improves the scores of antecedent F-measure while the BLEU scores were almost unchanged. Every effectiveness of the zero pronoun resolution differs depending on the type and case of each zero pronoun. © 2012 Association for Computational Linguistics.

关键词： 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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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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Analyzing English-Spanish Named-Entity enhanced Machine translation 9

Analyzing English-Spanish Named-Entity enhanced Machine Tran...

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9th workshop on syntax, semantics and structure in statistical translation, SSST 2015 at the NAACL HLT 2015

作者： Artetxe, Mikel Agirre, Eneko Alegria, Inaki Labaka, Gorka IXA NLP Group University of the Basque Country UPV/EHU

ISBN: (纸本)9781941643419

translation of named-entities (NEs) is an issue in SMT. In this paper we analyze the errors when translating NEs with a SMT system from English to Spanish. We train on Europarl and test on News Commentary, focusing on entities correctly recognized by an automatic NE recognition system. The automatic systems translate around 85% NEs correctly, leaving a small margin for improving performance. In addition, we implement a purpose-build NE translator and integrate it in the SMT system, yielding a small but significant improvement in BLEU score. Our analysis shows that, contrary to similar systems translating from Chinese to English, there was no improvement in NE translation, prompting further work. © 2015 Association for Computational Linguistics

关键词： Natural language processing systems

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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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Linguistically-enriched models for Bulgarian-to-English machine translation 6

Linguistically-enriched models for Bulgarian-to-English mach...

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6th workshop on syntax, semantics and structure in statistical translation, SSST 2012 at the 50th Annual Meeting of the Association for Computational Linguistics, ACT 2012

作者： Wang, Rui Osenova, Petya Simov, Kiril Language Technology Lab DFKI GmbH Saarbrücken Germany Linguistic Modelling Department IICT Bulgarian Academy of Sciences Sofia Bulgaria

ISBN: (纸本)9781937284381

In this paper, we present our linguistically-enriched Bulgarian-to-English statistical machine translation model, which takes a statistical machine translation (SMT) system as backbone various linguistic features as factors. The motivation is to take advantages of both the robustness of the SMT system and the rich linguistic knowledge from morphological analysis as well as the hand-crafted grammar resources. The automatic evaluation has shown promising results and our extensive manual analysis confirms the high quality of the translation the system delivers. The whole framework is also extensible for incorporating information provided by different sources. © 2012 Association for Computational Linguistics.

关键词： Machine translation

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Taste of Two Different Flavours: Which Manipuri Script Works Better for English-Manipuri Language Pair SMT Systems? 7

Taste of Two Different Flavours: Which Manipuri Script Works...

引用

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

作者： Singh, Thoudam Doren Gulmohor Cross Road No 9 Juhu Mumbai400049 India

ISBN: (纸本)9781937284473

The statistical machine translation (SMT) system heavily depends on the sentence aligned parallel corpus and the target language model. This paper points out some of the core issues on switching a language script and its repercussion in the phrase based statistical machine translation system development. The present task reports on the outcome of English- Manipuri language pair phrase based SMT task on two aspects - a) Manipuri using Bengali script, b) Manipuri using transliterated Meetei Mayek script. Two independent views on Bengali script based SMT and transliterated Meitei Mayek based SMT systems of the training data and language models are presented and compared. The impact of various language models is commendable in such scenario. The BLEU and NIST score shows that Bengali script based phrase based SMT (PBSMT) outperforms over the Meetei Mayek based English to Manipuri SMT system. However, subjective evaluation shows slight variation against the automatic scores. ©2013 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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