FRINK Linked Data Fragments server

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• W190884927 abstract "Abstract We propose an efficient dialogue management for an informa-tionnavigationsystembasedonadocument knowledge base. Itis expected that incorporation of appropriate N-best candidatesofASRandcontextualinformationwillimprovethesystemper-formance. The system also has several choices in generatingresponses or confirmations. In this paper, this selection is opti-mizedasminimizationofBayesriskbasedonrewardforcorrectinformation presentation and penalty for redundant turns. Wehave evaluated this strategy with our spoken dialogue system“Dialogue Navigator for Kyoto City”, which also has question-answering capability. Effectiveness of the proposed frameworkwas confirmed in the success rate of retrieval and the averagenumber of turns for information access. Index Terms : spoken dialogue system, dialogue management,Bayes risk 1. Introduction The target of spoken dialogue systems is being extended fromsimple databases such as flight information to general docu-ments including manuals and newspaper articles[1]. In suchsystems, the automatic speech recognition (ASR) result of theuser utterance is matched against a set of target documents us-ing the vector space model, and documents with high matchingscores are presented to the user. These kinds of applicationsare expected to be useful especially when retrieving informa-tion with keyboardless devices such as a PDA, a tablet PC anda car navigation system. We have developed “Dialogue Navi-gator for Kyoto City”, which can make interactive guidance byincorporating the question-answering (QA) function as well asgeneral document retrieval.In these systems, making confirmation is needed to elim-inate misunderstandings caused by ASR errors, but so manyredundant confirmations are bothersome to users. There havebeenmanystudiesthatdealwithefficientdialoguemanagementto make confirmation[2, 3, 4]. However, most of them are de-signed for relational database (RDB) query tasks, which havea definite set of keywords, and they are not directly applica-ble to document retrieval tasks, in which every word is used inmatching. In document retrieval tasks, therefore, it is more rea-sonable to make confirmation considering the confidence scoreof retrieval.In addition, our system classifies user utterances to queriesand questions, and generates appropriate responses for respec-tive inputs. Unlike conventional QA tasks, such as TREC QATrack[5], it is not obvious whether the utterance is a query orU1: Please explain the Golden Pavilion.S1: The Golden Pavilion is one of the buildings in theRokuon-ji in Kyoto, and is the main attraction of thetemple sites. The entire pavilion except the basementfloor is covered with pure gold leaf.U2: When was it built?S2: The Golden Pavilion was originally built in 1397to serve as a retirement villa for Shogun AshikagaYoshimitsu.U3: Then, please tell me its history.......Figure 1: Example dialogue of Dialogue Navigator for KyotoCitya question. In addition, an exact answer for a question doesnot necessarily exist in the document sets. Therefore, it is notalways optimal to respond the question with its answer alone.In this paper, we address the extension of conventional op-timization methods of dialogue management, to be applicableto general document retrieval tasks with QA function. Specif-ically, we propose a dialogue management that optimizes thechoicesinresponsegenerationbyminimizingBayesrisk,basedon reward for correct information presentation and penalty forredundant turns, which are defined by the score of documentretrieval and answer extraction." @default.
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• W190884927 date "2007-08-27" @default.
• W190884927 modified "2023-09-26" @default.
• W190884927 title "Bayes risk-based optimization of dialogue management for document retrieval system with speech interface" @default.
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• W190884927 doi "https://doi.org/10.21437/interspeech.2007-710" @default.
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