Concordance

Query dialogue 27 (1,258.8 per million)

other,8-1-H01-1017,bq	To support engaging human users in robust , <term> mixed-initiative speech dialogue interactions </term> which reach beyond current capabilities in <term> dialogue systems </term> , the <term> DARPA Communicator program </term> [ 1 ] is funding the development of a <term> distributed message-passing infrastructure </term> for <term> dialogue systems </term> which all <term> Communicator </term> participants are using .	#216 To support engaging human users in robust, mixed-initiative speech dialogue interactions which reach beyond current capabilities in dialogue systems, the DARPA Communicator program [1] is funding the development of a distributed message-passing infrastructure for dialogue systems which all Communicator participants are using.
tech,18-1-H01-1017,bq	To support engaging human users in robust , <term> mixed-initiative speech dialogue interactions </term> which reach beyond current capabilities in <term> dialogue systems </term> , the <term> DARPA Communicator program </term> [ 1 ] is funding the development of a <term> distributed message-passing infrastructure </term> for <term> dialogue systems </term> which all <term> Communicator </term> participants are using .	#224 To support engaging human users in robust, mixed-initiative speech dialogue interactions which reach beyond current capabilities indialogue systems, the DARPA Communicator program [1] is funding the development of a distributed message-passing infrastructure for dialogue systems which all Communicator participants are using.
tech,38-1-H01-1017,bq	To support engaging human users in robust , <term> mixed-initiative speech dialogue interactions </term> which reach beyond current capabilities in <term> dialogue systems </term> , the <term> DARPA Communicator program </term> [ 1 ] is funding the development of a <term> distributed message-passing infrastructure </term> for <term> dialogue systems </term> which all <term> Communicator </term> participants are using .	#244 To support engaging human users in robust, mixed-initiative speech dialogue interactions which reach beyond current capabilities in dialogue systems, the DARPA Communicator program [1] is funding the development of a distributed message-passing infrastructure fordialogue systems which all Communicator participants are using.
tech,8-1-H01-1068,bq	We describe a three-tiered approach for <term> evaluation </term> of <term> spoken dialogue systems </term> .	#1204 We describe a three-tiered approach for evaluation of spoken dialogue systems.
tech,12-2-P01-1056,bq	In this paper We experimentally evaluate a <term> trainable sentence planner </term> for a <term> spoken dialogue system </term> by eliciting <term> subjective human judgments </term> .	#2063 In this paper We experimentally evaluate a trainable sentence planner for a spoken dialogue system by eliciting subjective human judgments.
tech,16-1-N03-3010,bq	In this paper , we propose a novel <term> Cooperative Model </term> for <term> natural language understanding </term> in a <term> dialogue system </term> .	#3493 In this paper, we propose a novel Cooperative Model for natural language understanding in adialogue system.
other,11-1-P03-1022,bq	We apply a <term> decision tree based approach </term> to <term> pronoun resolution </term> in <term> spoken dialogue </term> .	#3985 We apply a decision tree based approach to pronoun resolution in spoken dialogue.
other,11-3-P03-1022,bq	We present a set of <term> features </term> designed for <term> pronoun resolution </term> in <term> spoken dialogue </term> and determine the most promising <term> features </term> .	#4010 We present a set of features designed for pronoun resolution in spoken dialogue and determine the most promising features.
tech,8-1-P03-1031,bq	This paper concerns the <term> discourse understanding process </term> in <term> spoken dialogue systems </term> .	#4135 This paper concerns the discourse understanding process in spoken dialogue systems.
other,15-2-P03-1031,bq	This process enables the <term> system </term> to understand <term> user utterances </term> based on the <term> context </term> of a <term> dialogue </term> .	#4153 This process enables the system to understand user utterances based on the context of adialogue.
other,13-4-P03-1031,bq	By holding multiple <term> candidates </term> for <term> understanding </term> results and resolving the <term> ambiguity </term> as the <term> dialogue </term> progresses , the <term> discourse understanding accuracy </term> can be improved .	#4205 By holding multiple candidates for understanding results and resolving the ambiguity as thedialogue progresses, the discourse understanding accuracy can be improved.
lr,15-5-P03-1031,bq	This paper proposes a method for resolving this <term> ambiguity </term> based on <term> statistical information </term> obtained from <term> dialogue corpora </term> .	#4231 This paper proposes a method for resolving this ambiguity based on statistical information obtained fromdialogue corpora.
tech,15-1-P03-1033,bq	We address appropriate <term> user modeling </term> in order to generate <term> cooperative responses </term> to each <term> user </term> in <term> spoken dialogue systems </term> .	#4295 We address appropriate user modeling in order to generate cooperative responses to each user in spoken dialogue systems.
lr,13-4-P03-1033,bq	Moreover , the <term> models </term> are automatically derived by <term> decision tree learning </term> using real <term> dialogue data </term> collected by the <term> system </term> .	#4364 Moreover, the models are automatically derived by decision tree learning using realdialogue data collected by the system.
other,21-7-P03-1033,bq	Experimental evaluation shows that the <term> cooperative responses </term> adaptive to <term> individual users </term> serve as good guidance for <term> novice users </term> without increasing the <term> dialogue duration </term> for <term> skilled users </term> .	#4424 Experimental evaluation shows that the cooperative responses adaptive to individual users serve as good guidance for novice users without increasing thedialogue duration for skilled users.
other,11-3-P03-1070,bq	The distribution of <term> nonverbal behaviors </term> differed depending on the type of <term> dialogue move </term> being grounded , and the overall pattern reflected a monitoring of lack of <term> negative feedback </term> .	#5069 The distribution of nonverbal behaviors differed depending on the type ofdialogue move being grounded, and the overall pattern reflected a monitoring of lack of negative feedback.
other,18-4-P03-1070,bq	Based on these results , we present an <term> ECA </term> that uses <term> verbal and nonverbal grounding acts </term> to update <term> dialogue state </term> .	#5105 Based on these results, we present an ECA that uses verbal and nonverbal grounding acts to updatedialogue state.
other,12-1-C04-1035,bq	This paper presents a <term> machine learning </term> approach to bare <term> sluice disambiguation </term> in <term> dialogue </term> .	#5161 This paper presents a machine learning approach to bare sluice disambiguation indialogue.
other,47-1-C04-1128,bq	While <term> sentence extraction </term> as an approach to <term> summarization </term> has been shown to work in <term> documents </term> of certain <term> genres </term> , because of the conversational nature of <term> email communication </term> where <term> utterances </term> are made in relation to one made previously , <term> sentence extraction </term> may not capture the necessary <term> segments </term> of <term> dialogue </term> that would make a <term> summary </term> coherent .	#6249 While sentence extraction as an approach to summarization has been shown to work in documents of certain genres, because of the conversational nature of email communication where utterances are made in relation to one made previously, sentence extraction may not capture the necessary segments ofdialogue that would make a summary coherent.
other,10-2-E06-1022,bq	First , we investigate how well the <term> addressee </term> of a <term> dialogue act </term> can be predicted based on <term> gaze </term> , <term> utterance </term> and <term> conversational context features </term> .	#10263 First, we investigate how well the addressee of adialogue act can be predicted based on gaze, utterance and conversational context features.


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