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Brief Stimulus Exposure Fully Remediates Temporal Processing Deficits Induced by Early Hearing Loss
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Multi-domain joint semantic frame parsing using bi-directional RNN-LSTM,” in INTERSPEECH
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In: https://www.microsoft.com/en-us/research/wp-content/uploads/2016/06/IS16_MultiJoint.pdf (2016)
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EVALUATION OF THE SIGNAL-TO-NOISE RATIO REQUIRED TO ACHIEVE THE SAME PERFORMANCE IN ENGLISH AND MANDARIN CHINESE
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Semi-supervised learning of semantic classes for query . . .
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In: http://research.microsoft.com/pubs/101154/fp0894-wang-webpost.pdf (2009)
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Acero: A discriminative training framework using N-best speech recognition transcriptions and scores for spoken utterance classification
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In: http://www1.icsi.berkeley.edu/~sibel/ICASSP2007SUC.pdf (2007)
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SGStudio: Rapid Semantic Grammar Development for Spoken Language Understanding
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In: http://research.microsoft.com/pubs/60452/2005-wang-acero-eurospeech.pdf (2005)
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Grammar Inference and Statistical Machine Translation
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In: http://www.is.cs.cmu.edu/papers/speech/phd-thesis/thesis-yyw.ps.gz (1998)
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Decoding Algorithm in Statistical Machine Translation
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In: http://www.ri.cmu.edu/pub_files/pub1/wang_ye_yi_1997_1/wang_ye_yi_1997_1.ps.gz (1997)
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Word Clustering With Parallel Spoken Language Corpora
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In: http://research.microsoft.com/users/yeyiwang/publications/icslp96.ps (1996)
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Word Clustering With Parallel Spoken Language Corpora
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In: http://www.ubka.uni-karlsruhe.de/vvv/1996/informatik/66/66.ps.gz (1996)
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Abstract:
In this paper we introduce a word clustering algorithm which uses a bilingual, parallel corpus to group together words in the source and target language. Our method generalizes previous mutual information clustering algorithms for monolingual data by incorporating a statistical translation model. Preliminary experiments have shown that the algorithm can effectively employ the constraints implicit in bilingual data to extract classes which are well-suited to machine translation tasks. 1. INTRODUCTION Language learning is a multi-modal process. Children can never learn a language by only reading a book, without any other input. Language learning is also a multi-channel process. A second language learner often uses the knowledge about his native language and the correspondence between the native language and the second language in acquiring new language ability. In this paper we investigate a method to automatically classify words by employing a parallel, bilingual corpus of text. Word .
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URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.26.9799 http://www.ubka.uni-karlsruhe.de/vvv/1996/informatik/66/66.ps.gz
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Word clustering with parallel spoken language corpora
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In: http://www.ri.cmu.edu/pub_files/pub1/wang_ye_yi_1996_1/wang_ye_yi_1996_1.pdf (1996)
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Word clustering with parallel spoken language corpora
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In: http://research.microsoft.com/pubs/75238/1996-yeyiwang-icslp.pdf (1996)
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Word Clustering With Parallel Spoken Language Corpora
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In: http://www.asel.udel.edu/icslp/cdrom/vol4/687/a687.pdf (1996)
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Improved Language Modeling By Unsupervised Acquisition Of Structure
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In: ftp://ftp.cs.cmu.edu/afs/cs/project/cmt-38/ries/ftp/ries_buo_wang_icassp95.ps.gz (1995)
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Improved Language Modeling By Unsupervised Acquisition Of Structure
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In: http://werner.ira.uka.de/papers/speech/1995/ICASSP_95_klaus_ries.ps.gz (1995)
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Connectionist Transfer in Machine Translation
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In: http://research.microsoft.com/users/yeyiwang/publications/ranlp95.ps (1995)
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Dual-coding theory and connectionist lexical selection
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In: http://arxiv.org/pdf/cmp-lg/9405035v1.pdf (1994)
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