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Translation-Based Projection for Multilingual Coreference Resolution 12

Translation-Based Projection for Multilingual Coreference Re...

引用

Conference of the North American Chapter of the Association for Computational Linguistics: human language technologies

作者： Altaf Rahman Vincent Ng University of Texas at Dallas Richardson TX

ISBN: (纸本)9781937284206

To build a coreference resolver for a new language, the typical approach is to first coreference-annotate documents from this target language and then train a resolver on these annotated documents using supervised learning techniques. However, the high cost associated with manually coreference-annotating documents needed by a supervised approach makes it difficult to deploy coreference technologies across a large number of natural languages. To alleviate this corpus annotation bottleneck, we examine a translation-based projection approach to multilingual coreference resolution. Experimental results on two target languages demonstrate the promise of our approach.

关键词： object oriented programming languages Coreference Resolution resolvers Language natural languages Projection

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Morpheme- and POS-based ibm1 scores and language model scores for translation quality estimation 12

Morpheme- and POS-based ibm1 scores and language model score...

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Workshop on Statistical Machine Translation

作者： Maja Popovic German Research Center for Artificial Intelligence (DFKI) Language Technology (LT) Berlin Germany

ISBN: (纸本)9781622765928

We present a method we used for the quality estimation shared task of wmt 2012 involving IBMl and language model scores calculated on morphemes and pos tags. The IBM 1 scores calculated on morphemes and POS-4grams of the source sentence and obtained translation output are shown to be competitive with the classic evaluation metrics for ranking of translation systems. Since these scores do not require any reference translations, they can be used as features for the quality estimation task presenting a connection between the source language and the obtained target language. In addition, target language model scores of morphemes and POS tags are investigated as estimates for the obtained target language quality.

关键词： object oriented programming languages modelling languages original language reduced mass Translation systems

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LIMSI @ WMT'12 12

LIMSI @ WMT'12

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Workshop on Statistical Machine Translation

作者： Hai-Son Le Thomas Lavergne Alexandre Allauzen Marianna Apidianaki Li Gong Aurélien Max Artem Sokolov Guillaume Wisniewski Fran?ois Yvon Univ. Paris-Sud LIMSI-CNRS

ISBN: (纸本)9781622765928;1622765923

This paper describes LIMSI's submissions to the shared translation task. We report results for French-English and German-English in both directions. Our submissions use n-code, an open source system based on bilingual n-grams. In this approach, both the translation and target language models are estimated as conventional smoothed n-gram models; an approach we extend here by estimating the translation probabilities in a continuous space using neural networks. Experimental results show a significant and consistent BLEU improvement of approximately 1 point for all conditions. We also report preliminary experiments using an "on-the-fly" translation model.

关键词： LIMSI submissions report Translations Translation Translation Process Belgo-Luxembourg Economic Union CONTINUOUS SPACE object oriented programming languages open source smoothing

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ONTS: 'Optima' News Translation System 12

ONTS: 'Optima' News Translation System

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Conference of The European Chapter of The Association for Computational Linguistics

作者： Marco Turchi Martin Atkinson Alastair Wilcox Brett Crawley Stefano Bucci Ralf Steinberger Erik Van der Goot European Commission - Joint Research Centre (JRC) IPSC - GlobeSec Via Fermi 2749 21020 Ispra (VA) - Italy

ISBN: (纸本)9781622765003

We propose a real-time machine translation system that allows users to select a news category and to translate the related live news articles from Arabic, Czech, Danish, Farsi, French, German, Italian, Polish, Portuguese, Spanish and Turkish into English. The Moses-based system was optimised for the news domain and differs from other available systems in four ways: (1) News items are automatically categorised on the source side, before translation; (2) Named entity translation is optimised by recognising and extracting them on the source side and by re-inserting their translation in the target language, making use of a separate entity repository; (3) News titles are translated with a separate translation system which is optimised for the specific style of news titles; (4) The system was optimised for speed in order to cope with the large volume of daily news articles.

关键词： news object oriented programming languages Translations Translation Translation Process large volume Turkish

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DCU-Symantec Submission for the WMT 2012 Quality Estimation Task 12

DCU-Symantec Submission for the WMT 2012 Quality Estimation ...

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Workshop on Statistical Machine Translation

作者： Raphael Rubino Jennifer Foster Joachim Wagner Johann Roturier Rasul Samad Zadeh Kaljahi Fred Hollowood Dublin City University Symantec Ireland

ISBN: (纸本)9781622765928;1622765923

This paper describes the features and the machine learning methods used by Dublin City University (DCU) and SYMANTEC for the WMT 2012 quality estimation task. Two sets of features are proposed: one constrained, i.e. respecting the data limitation suggested by the workshop organisers, and one unconstrained, i.e. using data or tools trained on data that was not provided by the workshop organisers. In total, more than 300 features were extracted and used to train classifiers in order to predict the translation quality of unseen data. In this paper, we focus on a subset of our feature set that we consider to be relatively novel: features based on a topic model built using the Latent Dirichlet Allocation approach, and features based on source and target language syntax extracted using part-of-speech (POS) taggers and parsers. We evaluate nine feature combinations using four classification-based and four regression-based machine learning techniques.

关键词： DCU-Symantec Submission Quality reduced mass Parser Artificial Intelligence Machine Learning Workshops workshops Classifiers object oriented programming languages dichloralurea taggers DCU Subset

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Rescoring a Phrase-based Machine Transliteration System with Recurrent Neural Network Language Models 12

Rescoring a Phrase-based Machine Transliteration System with...

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Named Entity Workshop

作者： Andrew Finch Paul Dixon Eiichiro Sumita NICT 3-5 Hikaridai Keihanna Science City 619-0289 JAPAN

ISBN: (纸本)9781627483476

The system entered into this year's shared transliteration evaluation is implemented within a phrase-based statistical machine transliteration (SMT) framework. The system is based on a joint source-channel model in combination with a target language model and models to control the length of the sequences generated. The joint source-channel model was trained using a many-to-many Bayesian bilingual alignment. The focus of this year's system is on input representation. In order attempt to mitigate data sparseness issues in the joint source-channel model, we augmented the system with recurrent neural network (RNN) models that can learn to project the grapheme set onto a smaller hidden representation. We performed experiments on development data to evaluate the effectiveness of our approach. Our results show that using an RNN language model can improve performance for language pairs with large grapheme sets on the target side.

关键词： recurrent neural nets modelling languages channel model machine Joints Language object oriented programming languages

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Adaptation of Statistical Machine Translation Model for Cross-Lingual Information Retrieval in a Service Context 12

Adaptation of Statistical Machine Translation Model for Cros...

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Conference of the European Chapter of the Association for Computational Linguistics

作者： Vassilina Nikoulina Bogomil Kovachev Nikolaos Lagos Christof Monz Xerox Research Center Europe Informatics Institute University of Amsterdam

ISBN: (纸本)9781937284190

This work proposes to adapt an existing general SMT model for the task of translating queries that are subsequently going to be used to retrieve information from a target language collection. In the scenario that we focus on access to the document collection itself is not available and changes to the IR model are not possible. We propose two ways to achieve the adaptation effect and both of them are aimed at tuning parameter weights on a set of parallel queries. The first approach is via a standard tuning procedure optimizing for BLEU score and the second one is via a reranking approach optimizing for MAP score. We also extend the second approach by using syntax-based features. Our experiments show improvements of 1-2.5 in terms of MAP score over the retrieval with the non-adapted translation. We show that these improvements are due both to the integration of the adaptation and syntax-features for the query translation task.

关键词： information retrieval machine translation manufacturing automation protocol MITIGATION ACTION PLANS object oriented programming languages Belgo-Luxembourg Economic Union tuning Not Available

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Syntax-aware Phrase-based Statistical Machine Translation: System Description 12

Syntax-aware Phrase-based Statistical Machine Translation: S...

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Workshop on Statistical Machine Translation

作者： Ulrich Germann University of Toronto Toronto Ontario Canada

ISBN: (纸本)9781622765928

We present a variant of phrase-based SMT that uses source-side parsing and a constituent reordering model based on word alignments in the word-aligned training corpus to predict hierarchical block-wise reordering of the input. Multiple possible translation orders are represented compactly in a source order lattice. This source order lattice is then annotated with phrase-level translations to form a lattice of tokens in the target language. Various feature functions are combined in a log-linear fashion to evaluate paths through that lattice.

关键词： crystal lattices object oriented programming languages machine translation system token Byways variants Translations Parsing Surface mount technology

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Bootstrapping Method for Chunk Alignment in Phrase Based SMT 2012

Bootstrapping Method for Chunk Alignment in Phrase Based SMT

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Conference of The European Chapter of The Association for Computational Linguistics

作者： Santanu Pal Sivaji Bandyopadhyay Department of Computer Science and Engineering Jadavpur University

ISBN: (纸本)9781622764709

The processing of parallel corpus plays very crucial role for improving the overall performance in Phrase Based Statistical Machine Translation systems (PB-SMT). In this paper the automatic alignments of different kind of chunks have been studied that boosts up the word alignment as well as the machine translation quality. Single-tokenization of Noun-noun MWEs, phrasal preposition (source side only) and reduplicated phrases (target side only) and the alignment of named entities and complex predicates provide the best SMT model for bootstrapping. Automatic bootstrapping on the alignment of various chunks makes significant gains over the previous best English-Bengali PB-SMT system. The source chunks are translated into the target language using the PB-SMT system and the translated chunks are compared with the original target chunk. The aligned chunks increase the size of the parallel corpus. The processes are run in a bootstrapping manner until all the source chunks have been aligned with the target chunks or no new chunk alignment is identified by the bootstrapping process. The proposed system achieves significant improvements (2.25 BLEU over the best System and 8.63 BLEU points absolute over the baseline system, 98.74% relative improvement over the baseline system) on an English- Bengali translation task.

关键词： bootstrapping Alignment object oriented programming languages Surface mount technology Belgo-Luxembourg Economic Union Chunking

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Source Language Adaptation for Resource-Poor Machine Translation 12

Source Language Adaptation for Resource-Poor Machine Transla...

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Conference on Empirical Methods in Natural Language Processing

作者： Pidong Wang Preslav Nakov Hwee Tou Ng Department of Computer Science National University of Singapore 13 Computing Drive Singapore 117417 QCRI Qatar Foundation Tornado Tower P.O. 5825 Doha Qatar

ISBN: (纸本)9781622765034

We propose a novel, language-independent approach for improving machine translation from a resource-poor language to X by adapting a large bi-text for a related resource-rich language and X (the same target language). We assume a small bi-text for the resource-poor language to X pair, which we use to learn word-level and phrase-level paraphrases and cross-lingual morphological variants between the resource-rich and the resource-poor language; we then adapt the former to get closer to the latter. Our experiments for Indonesian/Malay-English translation show that using the large adapted resource-rich bi-text yields 6.7 BLEU points of improvement over the unadapted one and 2.6 BLEU points over the original small bi-text. Moreover, combining the small bi-text with the adapted bi-text outperforms the corresponding combinations with the unadapted bi-text by 1.5-3 BLEU points. We also demonstrate applicability to other languages and domains.

关键词： machine translation Belgo-Luxembourg Economic Union Language original language object oriented programming languages closer

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