| measure(ment),8-3-H01-1040,bq | results of a preliminary , <term> qualitative | user | evaluation </term> of the <term> system </term> | #353 We also report results of a preliminary, qualitative user evaluation of the system, which while broadly positive indicates further work needs to be done on the interface to make users aware of the increased potential of IE-enhanced text browsers. |
| other,22-2-H01-1055,bq | </term> understand more of what the <term> | user | </term> tells them , they need to be more | #952 However, the improved speech recognition has brought to light a new problem: as dialog systems understand more of what theuser tells them, they need to be more sophisticated at responding to the user. |
| other,36-2-H01-1055,bq | sophisticated at responding to the <term> | user | </term> . The issue of <term> system response | #966 However, the improved speech recognition has brought to light a new problem: as dialog systems understand more of what the user tells them, they need to be more sophisticated at responding to theuser. |
| measure(ment),4-2-H01-1068,bq | systems </term> . The three tiers measure <term> | user | satisfaction </term> , <term> system support | #1211 The three tiers measureuser satisfaction, system support of mission success and component performance. |
| other,10-3-H01-1068,bq | this approach in numerous fielded <term> | user | studies </term> conducted with the U.S. military | #1233 We describe our use of this approach in numerous fieldeduser studies conducted with the U.S. military. |
| other,22-2-P01-1070,bq | target variables which represent a <term> | user | 's informational goals </term> . We report | #2165 These models, which are built from shallow linguistic features of questions, are employed to predict target variables which represent auser's informational goals. |
| other,7-2-P03-1031,bq | the <term> system </term> to understand <term> | user | utterances </term> based on the <term> context | #4145 This process enables the system to understanduser utterances based on the context of a dialogue. |
| other,12-3-P03-1031,bq | </term> result can be obtained for a <term> | user | utterance </term> due to the <term> ambiguity | #4167 Since multiple candidates for the understanding result can be obtained for auser utterance due to the ambiguity of speech understanding, it is not appropriate to decide on a single understandingresult after each user utterance. |
| other,34-3-P03-1031,bq | understandingresult </term> after each <term> | user | utterance </term> . By holding multiple <term> | #4189 Since multiple candidates for the understanding result can be obtained for a user utterance due to the ambiguity of speech understanding, it is not appropriate to decide on a single understandingresult after eachuser utterance. |
| tech,3-1-P03-1033,bq | effective . We address appropriate <term> | user | modeling </term> in order to generate <term> | #4282 We address appropriateuser modeling in order to generate cooperative responses to each user in spoken dialogue systems. |
| other,13-1-P03-1033,bq | cooperative responses </term> to each <term> | user | </term> in <term> spoken dialogue systems </term> | #4292 We address appropriate user modeling in order to generate cooperative responses to eachuser in spoken dialogue systems. |
| other,6-2-P03-1033,bq | Unlike previous studies that focus on <term> | user | </term> 's <term> knowledge </term> or typical | #4304 Unlike previous studies that focus onuser's knowledge or typical kinds of users, the user model we propose is more comprehensive. |
| model,16-2-P03-1033,bq | typical kinds of <term> users </term> , the <term> | user | model </term> we propose is more comprehensive | #4314 Unlike previous studies that focus on user's knowledge or typical kinds of users, theuser model we propose is more comprehensive. |
| model,8-3-P03-1033,bq | Specifically , we set up three dimensions of <term> | user | models </term> : <term> skill level </term> to | #4330 Specifically, we set up three dimensions ofuser models: skill level to the system, knowledge level on the target domain and the degree of hastiness. |
| tech,5-6-P03-1033,bq | Dialogue strategies </term> based on the <term> | user | modeling </term> are implemented in <term> | #4385 Dialogue strategies based on theuser modeling are implemented in Kyoto city bus information system that has been developed at our laboratory. |
| other,4-2-P05-3025,bq | </term> . The <term> method </term> allows a <term> | user | </term> to explore a <term> model </term> of <term> | #9852 The method allows auser to explore a model of syntax-based statistical machine translation (MT), to understand the model's strengths and weaknesses, and to compare it to other MT systems. |
| other,28-2-P06-4007,bq | users </term> need by analyzing how a <term> | user | </term> interacts with a system while gathering | #11682 FERRET utilizes a novel approach to Q/A known as predictive questioning which attempts to identify the questions (and answers) that users need by analyzing how auser interacts with a system while gathering information related to a particular scenario. |
| tech,6-2-J88-3002,bq | </term> . This paper explores the role of <term> | user | modeling </term> in such <term> systems </term> | #16043 This paper explores the role ofuser modeling in such systems. |
| model,8-3-J88-3002,bq | with a characterization of what a <term> | user | model </term> is and how it can be used . | #16057 It begins with a characterization of what auser model is and how it can be used. |
| model,6-4-J88-3002,bq | . The types of information that a <term> | user | model </term> may be required to keep about | #16073 The types of information that auser model may be required to keep about a user are then identified and discussed. |
