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检索条件"任意字段=Workshop on Syntax and Structure in Statistical Translation"

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Zero Pronoun Resolution can Improve the Quality of J-E translation 12

Zero Pronoun Resolution can Improve the Quality of J-E Trans...

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workshop on syntax and structure in statistical translation

作者： Hirotoshi Taira Katsuhito Sudoh Masaaki Nagata NTT Communication Science Laboratories 2-4 Hikaridai Seika-cho Keihanna Science City Kyoto 619-0237 Japan

ISBN: (纸本)9781627483452

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.

关键词： Pronouns translations translation machine translation Zero translation Process Resolving power English language English Japanese Belgo-Luxembourg Economic Union

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Towards a Predicate-Argument Evaluation for MT 12

Towards a Predicate-Argument Evaluation for MT

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workshop on syntax and structure in statistical translation

作者： Ondrej Bojar Dekai Wu Charles University in Prague Faculty of Mathematics and Physics Institute of Formal and Applied Linguistics HKUST Human Language Technology Center Department of Computer Science and Engineering Hong Kong University of Science and Technology

ISBN: (纸本)9781627483452

HMEANT (Lo and Wu, 2011a) is a manual MT evaluation technique that focuses on predicate-argument structure of the sentence. We relate HMEANT to an established linguistic theory, highlighting the possibilities of reusing existing knowledge and resources for interpreting and automating HMEANT. We apply HMEANT to a new language, Czech in particular, by evaluating a set of English-to-Czech MT systems. HMEANT proves to correlate with manual rankings at the sentence . level better than a range of automatic metrics. However, the main contribution of this paper is the identification of several issues of HMEANT annotation and our proposal on how to resolve them.

关键词： machine translation MT Linguistic Theory Probability manual evaluation method Adjudication Automation

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WSD for n-best reranking and local language modeling in SMT 12

WSD for n-best reranking and local language modeling in SMT

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workshop on syntax and structure in statistical translation

作者： Marianna Apidianaki Guillaume Wisniewski Artem Sokolov Aurelien Max Francois Yvon LIMSI-CNRS BP 133 F-91403 Orsay Cedex France Univ. Paris Sud BP 133 F-91403 Orsay Cedex France

ISBN: (纸本)9781627483452

We integrate semantic information at two stages of the translation process of a state-of-the-art SMT system. A Word Sense Disambiguation (WSD) classifier produces a probability distribution over the translation candidates of source words which is exploited in two ways. First, the probabilities serve to rerank a list of n-best translations produced by the system. Second, the WSD predictions are used to build a supplementary language model for each sentence, aimed to favor translations that seem more adequate in this specific sentential context. Both approaches lead to significant improvements in translation performance, highlighting the usefulness of source side disambiguation for SMT.

关键词： modelling languages Surface mount technology translation Process Probability distribution translations

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Application of Clause Alignment for statistical Machine translation 12

Application of Clause Alignment for Statistical Machine Tran...

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workshop on syntax and structure in statistical translation

作者： Svetla Koeva Borislav Rizov Ivelina Stoyanova Svetlozara Leseva Rositsa Dekova Angel Genov Ekaterina Tarpomanova Tsvetana Dimitrova Hristina Kukova Department of Computational Linguistics Institute for Bulgarian Language Bulgarian Academy of Sciences Sofia 1113 Bulgaria

ISBN: (纸本)9781627483452

The paper presents a new resource light flexible method for clause alignment which combines the Gale-Church algorithm with internally collected textual information. The method does not resort to any pre-developed linguistic resources which makes it very appropriate for resource light clause alignment. We experiment with a combination of the method with the original Gale-Church algorithm (1993) applied for clause alignment. The performance of this flexible method, as it will be referred to hereafter, is measured over a specially designed test corpus. The clause alignment is explored as means to provide improved training data for the purposes of statistical Machine translation (SMT). A series of experiments with Moses demonstrate ways to modify the parallel resource and effects on translation quality: (1) baseline training with a Bulgarian-English parallel corpus aligned at sentence level; (2) training based on parallel clause pairs; (3) training with clause reordering, where clauses in each source language (SL) sentence are reordered according to order of the clauses in the target language (TL) sentence. Evaluation is based on BLEU score and shows small improvement when using the clause aligned corpus.

关键词： machine translation Alignment reduced mass

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Unsupervised vs. supervised weight estimation for semantic MT evaluation metrics 12

Unsupervised vs. supervised weight estimation for semantic M...

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workshop on syntax and structure in statistical translation

作者： Chi-kiu Lo Dekai Wu HKUST Human Language Technology Center Department of Computer Science and Engineering Hong Kong University of Science and Technology

ISBN: (纸本)9781627483452

We present an unsupervised approach to estimate the appropriate degree of contribution of each semantic role type for semantic translation evaluation, yielding a semantic MT evaluation metric whose correlation with human adequacy judgments is comparable to that of recent supervised approaches but without the high cost of a human-ranked training corpus. Our new unsupervised estimation approach is motivated by an analysis showing that the weights learned from supervised training are distributed in a similar fashion to the relative frequencies of the semantic roles. Empirical results show that even without a training corpus of human adequacy rankings against which to optimize correlation, using instead our relative frequency weighting scheme to approximate the importance of each semantic role type leads to a semantic MT evaluation metric that correlates comparable with human adequacy judgments to previous metrics that require far more expensive human rankings of adequacy over a training corpus. As a result, the cost of semantic MT evaluation is greatly reduced.

关键词： Semantics weight estimation ADEQUACY machine translation MT Metronidazole Comparable

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CCG Syntactic Reordering Models for Phrase-based Machine translation 12

CCG Syntactic Reordering Models for Phrase-based Machine Tra...

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workshop on statistical Machine translation

作者： Dennis N. Mehay Chris Brew The Ohio State University Columbus OH USA Educational Testing Service Princeton NJ USA

ISBN: (纸本)9781622765928

statistical phrase-based machine translation requires no linguistic information beyond word-aligned parallel corpora (Zens et al., 2002; Koehn et al., 2003). Unfortunately, this linguistic agnosticism often produces un-grammatical translations. syntax, or sentence structure, could provide guidance to phrase-based systems, but the "non-constituent" word strings that phrase-based decoders manipulate complicate the use of most recursive syntactic tools. We address these issues by using Combinatory Categorial Grammar, or CCG, (Steedman, 2000), which has a much more flexible notion of constituency, thereby providing more labels for putative non-constituent multiword translation phrases. Using CCG parse charts, we train a syntactic analogue of a lexicalized reordering model by labelling phrase table entries with multiword labels and demonstrate significant improvements in translating between Urdu and English, two language pairs with divergent sentence structure.

关键词： machine translation syntax Sentence structure combined cycle power generation Urdu linguistic data Guidance

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Using Categorial Grammar to Label translation Rules 12

Using Categorial Grammar to Label Translation Rules

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workshop on statistical Machine translation

作者： Jonathan Weese Chris Callison-Burch Adam Lopez Human Language Technology Center of Excellence Johns Hopkins University

ISBN: (纸本)9781622765928;1622765923

Adding syntactic labels to synchronous context-free translation rules can improve performance, but labeling with phrase structure constituents, as in GHKM (Galley et al., 2004), excludes potentially useful translation rules. SAMT (Zollmann and Venugopal, 2006) introduces heuristics to create new non-constituent labels, but these heuristics introduce many complex labels and tend to add rarely-applicable rules to the translation grammar. We introduce a labeling scheme based on categorial grammar, which allows syntactic labeling of many rules with a minimal, well-motivated label set. We show that our labeling scheme performs comparably to SAMT on an Urdu-English translation task, yet the label set is an order of magnitude smaller, and translation is twice as fast.

关键词： Using Categorial translation translations translation labeling scheme syntax Heuristics translation Process Rule Fasting

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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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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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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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