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Next-gen sequencing identifies non-coding variation disrupting miRNA-binding sites in neurological disorders
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Natural SQL: Making SQL Easier to Infer from Natural Language Specifications
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Towards Robustness of Text-to-SQL Models against Synonym Substitution
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Study of central exclusive [Image: see text] production in proton-proton collisions at [Formula: see text] and 13TeV
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In: Eur Phys J C Part Fields (2020)
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The relationship between English proficiency and humour appreciation among English L1 users and Chinese L2 users of English
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The flowering of positive psychology in Foreign Language Teaching and Acquisition research
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Exploiting future word contexts in neural network language models for speech recognition
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Survival percentages of atraumatic restorative treatment (ART) restorations and sealants in posterior teeth: an updated systematic review and meta-analysis [<Journal>]
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DNB Subject Category Language
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Disparities in Diabetes Care Quality by English Language Preference in Community Health Centers
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In: Leung, LB; Vargas-Bustamante, A; Martinez, AE; Chen, X; & Rodriguez, HP. (2018). Disparities in Diabetes Care Quality by English Language Preference in Community Health Centers. Health Services Research, 53(1), 509 - 531. doi:10.1111/1475-6773.12590. UCLA: Retrieved from: http://www.escholarship.org/uc/item/40x4d7fn (2018)
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Phonetic and graphemic systems for multi-genre broadcast transcription
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Future word contexts in neural network language models
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Abstract:
Recently, bidirectional recurrent network language models (bi-RNNLMs) have been shown to outperform standard, unidirectional, recurrent neural network language models (uni-RNNLMs) on a range of speech recognition tasks. This indicates that future word context information beyond the word history can be useful. However, bi-RNNLMs pose a number of challenges as they make use of the complete previous and future word context information. This impacts both training efficiency and their use within a lattice rescoring framework. In this paper these issues are addressed by proposing a novel neural network structure, succeeding word RNNLMs (suRNNLMs). Instead of using a recurrent unit to capture the complete future word contexts, a feedforward unit is used to model a finite number of succeeding, future, words. This model can be trained much more efficiently than bi-RNNLMs and can also be used for lattice rescoring. Experimental results on a meeting transcription task (AMI) show the proposed model consistently outperformed uni-RNNLMs and yield only a slight degradation compared to bi-RNNLMs in N-best rescoring. Additionally, performance improvements can be obtained using lattice rescoring and subsequent confusion network decoding.
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URL: http://eprints.whiterose.ac.uk/152826/
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Phonetic and graphemic systems for multi-genre broadcast transcription ...
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Phonetic and graphemic systems for multi-genre broadcast transcription
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Wang, Yu; Chen, X; Gales, Mark. - : IEEE, 2018. : https://ieeexplore.ieee.org/document/8462353, 2018. : ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2018
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Investigating bidirectional recurrent neural network language models for speech recognition
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Search for dark matter produced in association with heavy-flavor quark pairs in proton-proton collisions at [Formula: see text]
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Developing Universal Dependencies for Mandarin Chinese
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In: The 12th Workshop on Asian Language Resources ; https://halshs.archives-ouvertes.fr/halshs-01509329 ; The 12th Workshop on Asian Language Resources, 2016, Osaka, Japan (2016)
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