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Improving statistical machine translation using word sense disambiguation

Improving statistical machine translation using word sense d...

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2007 Joint Conference on Empirical Methods in Natural language Processing and Computational Natural language Learning, EMNLP-CoNLL 2007

作者： Carpuat, Marine Wu, Dekai Department of Computer Science and Engineering Human Language Technology Center HKUST University of Science and Technology Clear Water Bay Hong Kong Hong Kong

We show for the first time that incorporating the predictions of a word sense disambiguation system within a typical phrase-based statistical machine translation (SMT) model consistently improves translation quality across all three different IWSLT Chinese-English test sets, as well as producing statistically significant improvements on the larger NIST Chinese-English MT task-and moreover never hurts performance on any test set, according not only to BLEU but to all eight most commonly used automatic evaluation metrics. Recent work has challenged the assumption that word sense disambiguation (WSD) systems are useful for SMT. Yet SMT translation quality still obviously suffers from inaccurate lexical choice. In this paper, we address this problem by investigating a new strategy for integrating WSD into an SMT system, that performs fully phrasal multi-word disambiguation. Instead of directly incorporating a Senseval-style WSD system, we redefine the WSD task to match the exact same phrasal translation disambiguation task faced by phrase-based SMT systems. Our results provide the first known empirical evidence that lexical semantics are indeed useful for SMT, despite claims to the contrary. © 2007 Association for Computational Linguistics.

关键词： Semantics

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Tree-structured model selection and simulated-data adaptation for environmental and speaker robust speech recognition

Tree-structured model selection and simulated-data adaptatio...

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ISCIT 2007 - 2007 International Symposium on Communications and Information Technologies

作者： Thatphithakkul, Nattanun Kruatrachue, Boontee Wutiwiwatchai, Chai Marukatat, Sanparith Boonpiam, Vataya Department of Computer Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok 10520 Thailand Human Language Technology Laboratory National Electronics and Computer Technology Center Pathumthani 12120 Thailand

ISBN: (纸本)1424409772

This paper proposes the use of tree-structured model selection and simulated-data in maximum likelihood linear regression (MLLR) adaptation for environment and speaker robust speech recognition. The objective of this work is to solve major problems in robust speech recognition system, namely unknown speaker and unknown environmental noise. The proposed solution is composed of two components. The first one is based on a tree-structured model for selecting a speaker-dependent model that best matches to the input speech. The second component uses simulated-data to adapt the selected acoustic model to fit with the unknown noise. The proposed technique can thus alleviate both problems simultaneously. Experimental results show that the proposed system achieves a higher recognition rate than the system using only the input speech in adaptation and the system using a multi-conditioned acoustic model. © 2007 IEEE.

关键词： Forestry

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A Mandarin lecture speech transcription system for speech summarization

A Mandarin lecture speech transcription system for speech su...

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IEEE Workshop on Automatic Speech Recognition and Understanding

作者： Ho Yin Chan Justin Jian Zhang Pascale Fung Lu Cao Human Language Technology Center Electronic and Computer Engineering University of Science and Technology (HKUST) Kowloon Hong Kong China

ISBN: (纸本)9781424417452

This paper introduces our work on mandarin lecture speech transcription. In particular, we present our work on a small database, which contains only 16 hours of audio data and 0.16 M words of text data. A range of experiments have been done to improve the performances of the acoustic model and the language model, these include adapting the lecture speech data to the reading speech data for acoustic modeling and the use of lecture conference paper, power points and similar domain web data for language modeling. We also study the effects of automatic segmentation, unsupervised acoustic model adaptation and language model adaptation in our recognition system. By using a 3timesRT multiple passes decoding strategy, we obtain 70.3% accuracy performance in our final system. Finally, we apply our speech transcription system into a SVM summarizer and obtain a ROUGE-L F-measure of 66.5%.

关键词： Natural languages Power system modeling Adaptation model Decoding Audio databases Speech recognition Training data Error analysis Testing humans

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Improving lecture speech summarization using rhetorical information

Improving lecture speech summarization using rhetorical info...

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IEEE Workshop on Automatic Speech Recognition and Understanding

作者： Justin Jian Zhang Ho Yin Chan Pascale Fung Human Language Technology Center Electronic and Computer Engineering University of Science and Technology (HKUST) Kowloon Hong Kong China

ISBN: (纸本)9781424417452

We propose a novel method of extractive summarization of lecture speech based on unsupervised learning of its rhetorical structure. We present empirical evidence showing that rhetorical structure is the underlying semantics which is then rendered in linguistic and acoustic/prosodic forms in lecture speech. We present a first thorough investigation of the relative contribution of linguistic versus acoustic features and show that, at least for lecture speech, what is said is more important than how it is said. We base our experiments on conference speeches and corresponding presentation slides as the latter is a faithful description of the rhetorical structure of the former. We find that discourse features from broadcast news are not applicable to lecture speech. By using rhetorical structure information in our summarizer, its performance reaches 67.87% ROUGE-L F-measure at 30% compression, surpassing all previously reported results. The performance is also superior to the 66.47% ROUGE-L F-measure of baseline summarization performance without rhetorical information. We also show that, despite a 29.7% character error rate in speech recognition, extractive summarization performs relatively well, underlining the fact that spontaneity in lecture speech does not affect the central meaning of lecture speech.

关键词： Broadcasting Data mining Principal component analysis humans Speech recognition Hidden Markov models Natural languages Unsupervised learning Underwater acoustics Error analysis

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Tree-structured model selection and simulated-data adaptation for environmental and speaker robust speech recognition

Tree-structured model selection and simulated-data adaptatio...

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2007 International Symposium on Communications and Information Technologies (ISCIT 2007), vol.3

作者： Nattanun Thatphithakkul Boontee Kruatrachue Chai Wutiwiwatchai Sanparith Marukatat Vataya Boonpiam Department of Computer Engineering King Mongkut''s Institute of Technology Ladkrabang Bangkok Thailand Human Language Technology Laboratory National Electronics and Computer Technology Center Pathum Thani Thailand

关键词： Speech recognition Working environment noise Loudspeakers Acoustic noise Maximum likelihood linear regression Speech enhancement Paper technology Noise robustness Computational modeling computer simulation

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A grammatical approach to understanding textual tables using two-dimensional SCFGs 21

A grammatical approach to understanding textual tables using...

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21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, COLING/ACL 2006

作者： Wu, Dekai Lee, Ken Wing Kuen Human Language Technology Center HKUST Department of Computer Science and Engineering University of Science and Technology Clear Water Bay Hong Kong

We present an elegant and extensible model that is capable of providing semantic interpretations for an unusually wide range of textual tables in documents. Unlike the few existing table analysis models, which largely rely on relatively ad hoc heuristics, our linguistically-oriented approach is systematic and grammar based, which allows our model (1) to be concise and yet (2) recognize a wider range of data models than others, and (3) disambiguate to a significantly finer extent the underlying semantic interpretation of the table in terms of data models drawn from relation database theory. To accomplish this, the model introduces Viterbi parsing under two-dimensional stochastic CFGs. The cleaner grammatical approach facilitates not only greater coverage, but also grammar extension and maintenance, as well as a more direct and declarative link to semantic interpretation, for which we also introduce a new, cleaner data model. In disambiguation experiments on recognizing relevant data models of unseen web tables from different domains, a blind evaluation of the model showed 60% precision and 80% recall. © 2006 Association for Computational Linguistics

关键词： Stochastic systems

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Boosting for Chinese named entity recognition 5

Boosting for Chinese named entity recognition

引用

5th SIGHAN Workshop on Chinese language Processing, co-located with COLING/ACL 2006

作者： Yu, Xiaofeng Carpuat, Marine Wu, Dekai Human Language Technology Center HKUST Department of Computer Science and Engineering University of Science and Technology Clear Water Bay Hong Kong Hong Kong

ISBN: (纸本)1932432701

We report an experiment in which a high-performance boosting based NER model originally designed for multiple European languages is instead applied to the Chinese named entity recognition task of the third SIGHAN Chinese language processing bakeoff. Using a simple character-based model along with a set of features that are easily obtained from the Chinese input strings, the system described employs boosting, a promising and theoretically well-founded machine learning method to combine a set of weak classifiers together into a final system. Even though we did no other Chinese-specific tuning, and used only one-third of the MSRA and CityU corpora to train the system, reasonable results are obtained. Our evaluation results show that 75.07 and 80.51 overall F-measures were obtained on MSRA and CityU test sets respectively. © 2006 Association for Computational Linguistics.

关键词： Adaptive boosting

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Automatic learning of Chinese English semantic structure mapping

Automatic learning of Chinese English semantic structure map...

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IEEE Spoken language technology Workshop

作者： Pascale Fung Wu Zhaojun Yang Yongsheng Dekai Wu Human Language Technology Center Department of Electronic and Computer EngineeringDepartment of Computer Science and Engineering University of Science and Technology (HKUST) Hong Kong China

We present twin results on Chinese semantic parsing, with application to English-Chinese cross- lingual verb frame acquisition. First, we describe two new state-of-the-art Chinese shallow semantic parsers leading to an F-score of 82.01 on simultaneous frame and argument boundary identification and labeling. Subsequently, we propose a model that applies the separate Chinese and English semantic parsers to learn cross-lingual semantic verb frame argument mappings with 89.3% accuracy. The only training data needed by this cross-lingual learning model is a pair of non-parallel monolingual Propbanks, plus an unannotated parallel corpus. We also present the first reported controlled comparison of maximum entropy and SVM approaches to shallow semantic parsing, using the Chinese data.

关键词： Natural languages Labeling Training data Error correction US Government humans computer science Application software Entropy Support vector machines

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Inversion transduction grammar coverage of arabic-english word alignment for tree-structured statistical machine translation

Inversion transduction grammar coverage of arabic-english wo...

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IEEE Spoken language technology Workshop

作者： Dekai Wu Marine Carpuat Yihai Shen HKUST Department of Computer Science and Engineering Human Language Technology Center Hong Kong China

We present the first known direct measurement of word alignment coverage on an Arabic-English parallel corpus using inversion transduction grammar constraints. While direct measurements have been reported for several European and Asian languages, to date no results have been available for Arabic or any Semitic language despite much recent activity on Arabic- English spoken language and text translation. Many recent syntax based statistical MT models operate within the domain of ITG expressiveness, often for efficiency reasons, so it has become important to determine the extent to which the ITG constraint assumption holds. Our results on Arabic provide further evidence that ITG expressiveness appears largely sufficient for core MT models.

关键词： Natural languages Decoding Context modeling Hidden Markov models Error analysis humans Marine technology computer science Oral communication Formal languages

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EARSCTS:A Chinese Telephony Conversational Corpus for Speech Processing

EARSCTS:A Chinese Telephony Conversational Corpus for Speech...

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第八届全国人机语音通讯学术会议

作者： Pascale Fung Christopher Cieri Human Language Technology Center Department of Electrical and Electronic Engineering University of Science and Technology Hong Kong Linguistic Data Consortium University of Permsylvania U.S.A

This paper describes the development and collection as well as initial analysis of the EARS (Effective, Affordable, Reusable Speech-to-text) Chinese telephony speech corpus (EARSCTS). The corpus contains 1206 ten-minute natural Mandarin conversations between either strangers or friends. The total amount of the corpus is 200 hours. There are 40 topics in all conversations and each conversation focuses on a single topic. All the speech data are recorded over public telephone networks, e.g., landline and cellular channels. All the speech data are annotated manually with standard Chinese characters (GBK) as well as specific mark-ups for spontaneous speech, time alignment information is also provided. This corpus can be used for conversational and spontaneous Mandarin speech recognition and other application-dependent tasks. The EARSCTS corpus is the largest and first of its kind for Mandarin conversational telephony speech, providing sufficient and diversified samples needed for speech training, testing, adaptation and development.

关键词： EARSCTS:A Chinese Telephony Conversational Corpus for Speech Processing

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