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Cross-stitched Multi-modal Encoders ...
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2
CoNLL 2018 Shared Task System Outputs
Zeman, Daniel; Potthast, Martin; Duthoo, Elie. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2018
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3
Bootstrapping Multilingual Intent Models via Machine Translation for Dialog Automation ...
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4
Bootstrapping Multilingual Intent Models via Machine Translation for Dialog Automation
Ruiz, Nicholas; Bangalore, Srinivas; Chen, John. - : European Association for Machine Translation, 2018
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5
Natural language generation in interactive systems
Stent, Amanda; Bangalore, Srinivas. - Cambridge [u.a.] : Cambridge Univ. Press, 2014
Leibniz-Zentrum Allgemeine Sprachwissenschaft
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6
Introduction
In: Natural language generation in interactive systems (2014), S. 1-9
Leibniz-Zentrum Allgemeine Sprachwissenschaft
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7
Enriching machine-mediated speech-to-speech translation using contextual information
In: Computer speech and language. - Amsterdam [u.a.] : Elsevier 27 (2013) 2, 492-508
OLC Linguistik
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8
Finite-state models for speech-based search on mobile devices
In: Natural language engineering. - Cambridge : Cambridge University Press 17 (2011) 2, 243-264
BLLDB
OLC Linguistik
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9
Supertagging : using complex lexical descriptions in natural language processing
Bangalore, Srinivas (Hrsg.). - Cambridge, Mass. [u.a.] : The MIT Press, 2010
UB Frankfurt Linguistik
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10
Phrase Based Decoding using a Discriminative Model
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11
Combining lexical, syntactic and prosodic cues for improved online dialog act tagging
In: Computer speech and language. - Amsterdam [u.a.] : Elsevier 23 (2009) 4, 407-422
BLLDB
OLC Linguistik
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12
Incremental parsing models for dialog task structure
In: Association for Computational Linguistics / European Chapter. Conference of the European Chapter of the Association for Computational Linguistics. - Menlo Park, Calif. : ACL 12 (2009), 94-102
BLLDB
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13
Effects of word confusion networks on voice search
In: Association for Computational Linguistics / European Chapter. Conference of the European Chapter of the Association for Computational Linguistics. - Menlo Park, Calif. : ACL 12 (2009), 238-245
BLLDB
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14
Learning the structure of task-driven human-human dialogs
In: Institute of Electrical and Electronics Engineers. IEEE transactions on audio, speech and language processing. - New York, NY : Inst. 16 (2008) 7, 1249-1259
BLLDB
OLC Linguistik
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15
Exploiting acoustic and syntactic features for automatic prosody labeling in a maximum entropy framework
In: Institute of Electrical and Electronics Engineers. IEEE transactions on audio, speech and language processing. - New York, NY : Inst. 16 (2008) 4, 797-811
BLLDB
OLC Linguistik
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16
MODELING THE INTONATION OF DISCOURSE SEGMENTS FOR IMPROVED ONLINE DIALOG ACT TAGGING
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17
Exploiting Acoustic and Syntactic Features for Automatic Prosody Labeling in a Maximum Entropy Framework
Abstract: In this paper, we describe a maximum entropy-based automatic prosody labeling framework that exploits both language and speech information. We apply the proposed framework to both prominence and phrase structure detection within the Tones and Break Indices (ToBI) annotation scheme. Our framework utilizes novel syntactic features in the form of supertags and a quantized acoustic–prosodic feature representation that is similar to linear parameterizations of the prosodic contour. The proposed model is trained discriminatively and is robust in the selection of appropriate features for the task of prosody detection. The proposed maximum entropy acoustic–syntactic model achieves pitch accent and boundary tone detection accuracies of 86.0% and 93.1% on the Boston University Radio News corpus, and, 79.8% and 90.3% on the Boston Directions corpus. The phrase structure detection through prosodic break index labeling provides accuracies of 84% and 87% on the two corpora, respectively. The reported results are significantly better than previously reported results and demonstrate the strength of maximum entropy model in jointly modeling simple lexical, syntactic, and acoustic features for automatic prosody labeling.
Keyword: Article
URL: https://doi.org/10.1109/TASL.2008.917071
http://www.ncbi.nlm.nih.gov/pubmed/19603083
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2709295
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18
The AT&T spoken language understanding system
In: Institute of Electrical and Electronics Engineers. IEEE transactions on audio, speech and language processing. - New York, NY : Inst. 14 (2006) 1, 213-222
BLLDB
OLC Linguistik
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19
Introduction to the special issue on spoken language understanding in conversational systems
In: Speech communication. - Amsterdam [u.a.] : Elsevier 48 (2006) 3-4, 233-238
BLLDB
OLC Linguistik
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20
Edit machines for robust multimodal language processing
In: Association for Computational Linguistics / European Chapter. Conference of the European Chapter of the Association for Computational Linguistics. - Menlo Park, Calif. : ACL 11 (2006), 361-369
BLLDB
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