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Cross-stitched Multi-modal Encoders ...
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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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Bootstrapping Multilingual Intent Models via Machine Translation for Dialog Automation ...
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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
Abstract: In this paper, we present an approach to statistical machine translation that combines the power of a discriminative model (for training a model for Machine Translation), and the standard beam-search based decoding technique (for the translation of an input sentence). A discriminative approach for learning lexical selection and reordering utilizes a large set of feature functions (thereby providing the power to incorporate greater contextual and linguistic information), which leads to an effective training of these models. This model is then used by the standard state-of-art Moses decoder (Koehn et al., 2007) for the translation of an input sentence. We conducted our experiments on Spanish-English language pair. We used maximum entropy model in our experiments. We show that the performance of our approach (using simple lexical features) is comparable to that of the state-of-art statistical MT system (Koehn et al., 2007). When additional syntactic features (POS tags in this paper) are used, there is a boost in the performance which is likely to improve when richer syntactic features are incorporated in the model. ; the Fourth Workshop on Syntax and Structure in Statistical Translation (SSST-4) , 28 Aug 2010, 28 Aug 2010, See also: ADA640443
Keyword: 10005; 10012; accuracy; algorithms; computational linguistics; machine learning; machine translation; natural language computing; natural language understanding; probability distributions
URL: http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=AD1028958
http://www.dtic.mil/docs/citations/AD1028958
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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
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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
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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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