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The RWTH Aachen Machine Translation System for WMT 2012 7

The RWTH Aachen Machine Translation System for WMT 2012

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7th Workshop on Statistical Machine Translation, WMT 2012, immediately following the Conference of the North-American Chapter of the Association for Computational Linguistics - human language Technologies, NAACL HLT 2012

作者： Huck, Matthias Peitz, Stephan Freitag, Markus Nuhn, Malte Ney, Hermann Computer Science Department Human Language Technology And Pattern Recognition Group Rwth Aachen University AachenD-52056 Germany

ISBN: (纸本)9781937284206

This paper describes the statistical machine translation (SMT) systems developed at RWTH Aachen University for the translation task of the NAACL 2012 Seventh Workshop on Statistical Machine Translation (WMT 2012). We participated in the evaluation campaign for the French-English and German-English language pairs in both translation directions. Both hierarchical and phrase-based SMT systems are applied. A number of different techniques are evaluated, including an insertion model, different lexical smoothing methods, a discriminative reordering extension for the hierarchical system, reverse translation, and system combination. By application of these methods we achieve considerable improvements over the respective baseline systems. ©2012 Association for Computational Linguistics.

关键词： Hierarchical systems

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Improved combinatory categorial grammar induction with boundary words and bayesian inference

Improved combinatory categorial grammar induction with bound...

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24th International Conference on Computational Linguistics, COLING 2012

作者： Huang, Yun Zhang, Min Tan, Chew Lim Department of Computer Science National University of Singapore 13 Computing Drive Singapore Institute for Infocomm Research Human Language Department 1 Fusionopolis Way Singapore

Combinatory Categorial Grammar (CCG) is an expressive grammar formalism which is able to capture long-range dependencies. However, building large and wide-coverage treebanks for CCG is expensive and time-consuming. In this paper, we focus on the problem of unsupervised CCG induction from plain texts. Based on the baseline model in (Bisk and Hockenmaier, 2012), we propose following two improvements: (1) we utilize boundary part-of-speech (POS) tags to capture lexical information;(2) we perform nonparametric Bayesian inference based on the Pitman-Yor process to learn compact grammars. Experiments on English Penn treebank demonstrate the effectiveness of our boundary model and Bayesian learning. © 2012 The COLING.

关键词： Bayesian networks

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Entity clustering across languages

Entity clustering across languages

引用

2012 Conference of the North American Chapter of the Association for Computational Linguistics: human language Technologies, NAACL HLT 2012

作者： Green, Spence Andrews, Nicholas Gormley, Matthew R. Dredze, Mark Manning, Christopher D. Computer Science Department Stanford University United States Human Language Technology Center of Excellence Johns Hopkins University United States

ISBN: (纸本)1937284204

Standard entity clustering systems commonly rely on mention (string) matching, syntactic features, and linguistic resources like English WordNet. When co-referent text mentions appear in different languages, these techniques cannot be easily applied. Consequently, we develop new methods for clustering text mentions across documents and languages simultaneously, producing cross-lingual entity clusters. Our approach extends standard clustering algorithms with cross-lingual mention and context similarity measures. Crucially, we do not assume a pre-existing entity list (knowledge base), so entity characteristics are unknown. On an Arabic-English corpus that contains seven different text genres, our best model yields a 24.3% F1 gain over the baseline. © 2012 Association for Computational Linguistics.

关键词： Clustering algorithms

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Improved Constituent Context model with features

Improved Constituent Context model with features

引用

26th Pacific Asia Conference on language, Information and Computation, PACLIC 2012

作者： Huang, Yun Zhang, Min Tan, Chew Lim Human Language Department Institute for Infocomm Research 1 Fusionopolis Way Singapore Singapore Department of Computer Science National University of Singapore 13 Computing Drive Singapore Singapore

ISBN: (纸本)9789791421171

The Constituent-Context Model (CCM) achieves promising results for unsupervised grammar induction. However, its performance drops for longer sentences. In this paper, we describe a general feature-based model for CCM, in which linguistic knowledge can be easily integrated as features. Features take the log-linear form with local normalization, so the Expectation-Maximization (EM) algorithm is still applicable to estimate model parameters. The l1-norm is used to control the model complexity, leading to sparse and compact grammar. We also propose to use a separated development to perform model selection and an additional test set to evaluate the performance. Under this framework, we could automatically choose suitable model parameters rather than setting them empirically. Experiments on the English treebank demonstrate that the feature-based model achieves comparable performance on short sentences but significant improvement on longer sentences. © 2012 The PACLIC.

关键词： Maximum principle

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Combining confidence score and Mal-rule filters for automatic creation of Bangla error corpus: Grammar checker perspective

Combining confidence score and Mal-rule filters for automati...

引用

13th Annual Conference on Intelligent Text Processing and Computational Linguistics, CICLing 2012

作者： Kundu, Bibekananda Chakraborti, Sutanu Choudhury, Sanjay Kumar Language Technology Centre for Development of Advance Computing Kolkata 700091 India Department of Computer Science and Engineering Indian Institution of Technology Chennai 600036 India

ISBN: (纸本)9783642286001

This paper describes a novel approach for automatic creation of Bangla error corpus for training and evaluation of grammar checker systems. The procedure begins with automatic creation of large number of erroneous sentences from a set of grammatically correct sentences. A statistical Confidence Score Filter has been implemented to select proper samples from the generated erroneous sentences such that sentences with less probable word sequences get lower confidence score and vice versa. Rule based Mal-rule filter with HMM based semi-supervised POS tagger has been used to collect the sentences having improper tag sequences. Combination of these two filters ensures the robustness of the proposed approach such that no valid construction is getting selected within the synthetically generated error corpus. Though the present work focuses on the most frequent grammatical errors in Bangla written text, detail taxonomy of grammatical errors in Bangla is also presented here, with an aim to increase the coverage of the error corpus in future. The proposed approach is language independent and could be easily applied for creating similar corpora in other languages. © 2012 Springer-Verlag.

关键词： Errors

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Cross-Lingual language modeling with syntactic reordering for low-resource speech recognition

Cross-Lingual language modeling with syntactic reordering fo...

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2012 Joint Conference on Empirical Methods in Natural language Processing and Computational Natural language Learning, EMNLP-CoNLL 2012

作者： Xu, Ping Fung, Pascale Human Language Technology Center Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Hong Kong Hong Kong

ISBN: (纸本)9781937284435

This paper proposes cross-lingual language modeling for transcribing source resource-poor languages and translating them into target resource-rich languages if necessary. Our focus is to improve the speech recognition performance of low-resource languages by leveraging the language model statistics from resource-rich languages. The most challenging work of cross-lingual language modeling is to solve the syntactic discrepancies between the source and target languages. We therefore propose syntactic reordering for cross-lingual language modeling, and present a first result that compares inversion transduction grammar (ITG) reordering constraints to IBM and local constraints in an integrated speech transcription and translation system. Evaluations on resource-poor Cantonese speech transcription and Cantonese to resource-rich Mandarin translation tasks show that our proposed approach improves the system performance significantly, up to 3.4% relative WER reduction in Cantonese transcription and 13.3% relative bilingual evaluation understudy (BLEU) score improvement in Mandarin transcription compared with the system without reordering. © 2012 Association for Computational Linguistics.

关键词： Syntactics

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EMPIRICAL MEASUREMENTS ON A SESOTHO TONE LABELING ALGORITHM 3

EMPIRICAL MEASUREMENTS ON A SESOTHO TONE LABELING ALGORITHM

引用

3rd Workshop on Spoken language Technologies for Under-Resourced languages, SLTU 2012

作者： Raborife, Mpho Zerbian, Sabine Ewert, Sigrid Meraka Institute Human Language Technology Group South Africa Potsdam University Department of Linguistics Germany University of the Witwatersrand School of Computer Science South Africa

This article discusses the empirical assessments employed on two versions of a Sesotho tone labeling algorithm. This algorithm uses linguistically-defined Sesotho tonal rules to predict the tone labels on the syllables of Sesotho words. The two versions differed in the number of tonal rules that they employ as well the lexical categories that the tone rules apply to. Both versions were tested on the same input text and we employed t-tests to prove that one version is an improvement on the other. © SLTU 2012. All Rights Reserved.

关键词： Algorithm Sesotho Tone

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Semantic cohesion model for phrase-based SMT

Semantic cohesion model for phrase-based SMT

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24th International Conference on Computational Linguistics, COLING 2012

作者： Feng, Minwei Sun, Weiwei Ney, Hermann Computer Science Department Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany MOE Key Laboratory of Computational Linguistics Institute of Computer Science and Technology Peking University Beijing China

In this paper, we propose a novel semantic cohesion model. Our model utilizes the predicateargument structures as soft constraints and plays the role as a reordering model in the phrasebased statistical machine translation system. We build a translation system with GALE data. Experimental results on the NIST02, NIST03, NIST04, NIST05 and NIST08 Chinese-English tasks show that our model improves the baseline system by 0.93 BLEU 0.98 TER on average. We also compare our method with a syntax-augmented model (Cherry, 2008), and demonstrate the importance of predicate-argument semantics in machine translation. © 2012 The COLING.

关键词： Semantics

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RESOURCE development AND EXPERIMENTS IN AUTOMATIC SOUTH AFRICAN BROADCAST NEWS TRANSCRIPTION 3

RESOURCE DEVELOPMENT AND EXPERIMENTS IN AUTOMATIC SOUTH AFRI...

引用

3rd Workshop on Spoken language Technologies for Under-Resourced languages, SLTU 2012

作者： Kamper, Herman de Wet, Febe Hain, Thomas Niesler, Thomas Department of Electrical and Electronic Engineering Stellenbosch University South Africa Human Language Technology Competency Area CSIR Meraka Institute Pretoria South Africa Department of Computer Science University of Sheffield United Kingdom

We present a description of the development and evaluation of a first South African broadcast news transcription system. We describe a number of speech resources which have been collected in the resource-scarce South African environment for system development purposes: a 20 hour corpus of South African English (SAE) broadcast news;a 109M word corpus of South African newspaper text collected for language modelling purposes;and a 60k word SAE pronunciation dictionary. The development of our system is based on similar state-of-the-art broadcast news transcription systems and uses cross-word triphone HMMs, MF-PLP features and per-segment cepstral mean and per-bulletin cepstral variance normalisation. Our final system achieves a word error rate of 24.6%. We find that reasonable performance is achieved on newsreader speech while poor performance is achieved on spontaneous and telephone speech in our test data. Finally, we consider the recognition of MP3-compressed audio and show that performance deteriorates only at low bit-rates. © SLTU 2012. All Rights Reserved.

关键词： Modeling languages

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Name phylogeny: A generative model of string variation

Name phylogeny: A generative model of string variation

引用

2012 Joint Conference on Empirical Methods in Natural language Processing and Computational Natural language Learning, EMNLP-CoNLL 2012

作者： Andrews, Nicholas Eisner, Jason Dredze, Mark Department of Computer Science Human Language Technology Center of Excellence Johns Hopkins University 3400 N. Charles St. Baltimore MD 21218 United States

ISBN: (纸本)9781937284435

Many linguistic and textual processes involve transduction of strings. We show how to learn a stochastic transducer from an unorganized collection of strings (rather than string pairs). The role of the transducer is to organize the collection. Our generative model explains similarities among the strings by supposing that some strings in the collection were not generated ab initio, but were instead derived by transduction from other, "similar" strings in the collection. Our variational EM learning algorithm alternately reestimates this phylogeny and the transducer parameters. The final learned transducer can quickly link any test name into the final phylogeny, thereby locating variants of the test name. We find that our method can effectively find name variants in a corpus of web strings used to refer to persons in Wikipedia, improving over standard untrained distances such as Jaro-Winkler and Leven-shtein distance. © 2012 Association for Computational Linguistics.

关键词： Stochastic systems

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