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A Spoken Dialogue System for
   Electronic Program Guide Information Access
                                                                       Seokhwan Kim, Cheongjae Lee, Sangkeun Jung, and Gary Geunbae Lee
                                                                       Pohang University of Science and Technology (POSTECH), South Korea

                          ABSTRACT                                                          AUTOMATIC SPEECH                                                                                   SPOKEN LANGUAGE                                                    EPG DATABASE MANAGER
  In this paper, we present POSTECH Spoken Dialogue System                                    RECOGNIZER                                                                                        UNDERSTANDING                                                  The main purpose of the EPG database manager is to build
 for Electronic Program Guide Information Access (POSSDS-                                                                                                                                                                                                     a content database for the other modules in POSSDS-EPG
 EPG). POSSDS-EPG consists of automatic speech recognizer,                    To build the language model, the candidate utterances that                                           The SLU module of POSSDS-EPG was constructed by a
                                                                                                                                                                                                                                                              with minimal human effort.
 spoken language understanding, dialogue manager, system                     have high probability of being spoken by users are required. We                                      concept spotting approach which aims to extract only the
                                                                                                                                                                                                                                                               We chose an EPG website (http://www.epg.co.kr) dealing
 utterance generator, text-to-speech synthesizer, and EPG                    generate the candidate utterances automatically by using the                                         essential information for predefined meaning representation
                                                                                                                                                                                                                                                              with the information on Korean TV programs. The EPG
 database manager. Each module is designed and implemented                   dialogue examples in the existing example database and the                                           slots. The semantic frame is made up of these slots including
                                                                                                                                                                                                                                                              database manager builds a contents database from the
 to make an effective and practical spoken dialogue system. In               retrieved result from the up-to-date EPG database.                                                   dialogue act, main action, and component slots for the EPG
                                                                                                                                                                                                                                                              information on the website.
 particular, in order to reflect the up-to-date EPG information                                                                                                                   domain.
                                                                               An Existing Utterance                                                                               We regarded the SLU problem as a classification problem,
 which is updated frequently and periodically, we applied a web-                                                                                                                                                                                                     WEB PAGES
                                                                               I want to watch drama Hae-Sin around .                                                             which can be solved by statistical machine learning frame-
 mining technology to the EPG database manager, which builds                   [genre = drama], [program_name = Hae-Sin], [time = 9 pm]
 the content database based on automatically extracted                                                                                                                            works. To build a statistical model for the SLU problem, we
                                                                               Retrieved Results                                                                                                                                                                       Contents       Contents

 information from popular EPG websites. The automatically                      [genre = movie], [program_name = Monster], [time = 11 pm]                                          should prepare the training corpus containing utterances that                        Filtering       Tables


 generated content database is used by other modules in the                    [genre = sports], [program_name = Basketball], [time = 7 pm]                                       have high probability of being spoken by users. We can easily
                                                                               Candidate Utterances                                                                               create a training corpus by reusing the candidate utterances that                                 Information    Extracted
 system for building their own resources. Evaluations show that                                                                                                                                                                                                                      Extraction   Information
                                                                               I want to watch movie Monster around .                                                             are used for building the language model in the speech
 our system performs EPG access task in high performance and
                                                                               I want to watch sports Basketball around .                                                         recognizer.
 can be managed with low cost.                                                                                                                                                                                                                                                                    Building
                                                                                                                                                                                                                                                                                                                EPG DB
                                                                                                                                                                                                                                                                                                    DB




      POSSDS-EPG: POSTECH                                                                   DIALOGUE MANAGER                                                                                   SYSTEM UTTERANCE                                                                    EVALUATIONS
    SPOKEN DIALOGUE SYSTEM                                                                                                                                                                        GENERATOR                                                                             Manually      Automatically Man
                                                                              To develop an effective and practical spoken dialogue system,                                                                                                                        Evaluation
        FOR EPG DOMAIN                                                       we proposed the situation-based dialogue management method                                                                                                                                TCR
                                                                                                                                                                                                                                                                                     Managed System
                                                                                                                                                                                                                                                                                          0.76
                                                                                                                                                                                                                                                                                                         aged System
                                                                                                                                                                                                                                                                                                             0.72
                                                                                                                                                                                    The system utterance generator generates the literal sys-tem
                                                                             using dialogue examples. For the system utterance generation,                                                                                                                             STR                0.65               0.62
 POSSDS-EPG consists of a set of appropriate modules that are                                                                                                                      utterances based on the system action tag and the utterance
                                                                             we automatically construct and index a dialogue example                                                                                                                                  MRA                 0.85               0.85
designed to be connected to each other according to the order. The                                                                                                                 generating template. Each system action tag has at least one
                                                                             database from the dialogue corpus. The dialogue manager                                                                                                                             User Satisfaction        0.75               0.73
overall system aims to output the synthesized spoken response                                                                                                                      utterance generating template which is constructed manually.                 TCR: User Perception of Task Completion Rate
                                                                             retrieves the best dialogue example for the current dialogue
corresponding to an input utterance spoken by the user..                                                                                                                           The system utterance generating task is advanced by filling                  STR: Success Turn Rate
                                                                             situation, which includes a current user utterance, semantic
                                                                                                                                                                                   slots in the template with proper values, such as retrieving                 MRA: Mean Recognition Accuracy
                                                                             frame and discourse history. From the retrieved result, the                                                                                                                        User Satisfaction = aTCR + bSTR + rMRA
                                                                                                                                                                                   results from the EPG database, slot values in the semantic
                                                                             dialogue manager determines the system action tag from the
  User Utterance           ASR                   Language                                                                                                                          frame, and constituents in the discourse history.
                                                 MODEL                       pre-defined tag set.
                                                                                                                                                                                                                                                                          IMPLEMENTATION
                                          NLU
                           SLU
                                         MODEL                 WEB
                                                                                                                                                                Dialogue              System Action Tag    Inform_Channel
                              Semantic
         Meta-Rules           Frame                                                         User’s Utterance                                                    Corpus
                                                                                                                                                                                                           [program_name]은 [channel]에서 합니다.
          For DM
                         Dialogue                                                                                                                                     Automatic       Utterance Template   ( [program_name] eun [channel] e-seo hap-ni-da )
                         Manager                                                                               User         Semantic          Discourse               Indexing                             [program_name] is broadcasted on [channel].
         Dialogue                                                                                            Intention       Frame             History
        Example DB            System                                             System         Domain                                                                                   Slot Values       [program_name = 해신, channel = KBS]
                              Action                                                            Expert
                                                            EPG DB              Responses                                                                       Dialogue
                                                                                                                 Query Generation
         Meta-Rules   System Response      EPG DB
                                                            Manager
                                                                                                                                                               Example DB                                  해신은 KBS에서 합니다.
          For SRG        Generator                                                                                                                                                     System Utterance    ( Hae-Sin eun KBS e-seo hap-ni-da )
                                                                                                                   Utterance Similarity                   Retrieval                                        Hae-Sin is broadcasted on KBS.
                                                                                                                    Lexico-semantic Similarity
                                                                                                                    Discourse history Similarity
                                                                                             Best Dialogue                                                     Dialogue
                           TTS                      System Utterance                                                                                           Examples
                                                                                               Example                   Tie-breaking



           Overview of POSSDS-EPG System Architecture

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A spoken dialog system for electronic program guide information access

  • 1. A Spoken Dialogue System for Electronic Program Guide Information Access Seokhwan Kim, Cheongjae Lee, Sangkeun Jung, and Gary Geunbae Lee Pohang University of Science and Technology (POSTECH), South Korea ABSTRACT AUTOMATIC SPEECH SPOKEN LANGUAGE EPG DATABASE MANAGER In this paper, we present POSTECH Spoken Dialogue System RECOGNIZER UNDERSTANDING The main purpose of the EPG database manager is to build for Electronic Program Guide Information Access (POSSDS- a content database for the other modules in POSSDS-EPG EPG). POSSDS-EPG consists of automatic speech recognizer, To build the language model, the candidate utterances that The SLU module of POSSDS-EPG was constructed by a with minimal human effort. spoken language understanding, dialogue manager, system have high probability of being spoken by users are required. We concept spotting approach which aims to extract only the We chose an EPG website (http://www.epg.co.kr) dealing utterance generator, text-to-speech synthesizer, and EPG generate the candidate utterances automatically by using the essential information for predefined meaning representation with the information on Korean TV programs. The EPG database manager. Each module is designed and implemented dialogue examples in the existing example database and the slots. The semantic frame is made up of these slots including database manager builds a contents database from the to make an effective and practical spoken dialogue system. In retrieved result from the up-to-date EPG database. dialogue act, main action, and component slots for the EPG information on the website. particular, in order to reflect the up-to-date EPG information domain. An Existing Utterance We regarded the SLU problem as a classification problem, which is updated frequently and periodically, we applied a web- WEB PAGES I want to watch drama Hae-Sin around . which can be solved by statistical machine learning frame- mining technology to the EPG database manager, which builds [genre = drama], [program_name = Hae-Sin], [time = 9 pm] the content database based on automatically extracted works. To build a statistical model for the SLU problem, we Retrieved Results Contents Contents information from popular EPG websites. The automatically [genre = movie], [program_name = Monster], [time = 11 pm] should prepare the training corpus containing utterances that Filtering Tables generated content database is used by other modules in the [genre = sports], [program_name = Basketball], [time = 7 pm] have high probability of being spoken by users. We can easily Candidate Utterances create a training corpus by reusing the candidate utterances that Information Extracted system for building their own resources. Evaluations show that Extraction Information I want to watch movie Monster around . are used for building the language model in the speech our system performs EPG access task in high performance and I want to watch sports Basketball around . recognizer. can be managed with low cost. Building EPG DB DB POSSDS-EPG: POSTECH DIALOGUE MANAGER SYSTEM UTTERANCE EVALUATIONS SPOKEN DIALOGUE SYSTEM GENERATOR Manually Automatically Man To develop an effective and practical spoken dialogue system, Evaluation FOR EPG DOMAIN we proposed the situation-based dialogue management method TCR Managed System 0.76 aged System 0.72 The system utterance generator generates the literal sys-tem using dialogue examples. For the system utterance generation, STR 0.65 0.62 POSSDS-EPG consists of a set of appropriate modules that are utterances based on the system action tag and the utterance we automatically construct and index a dialogue example MRA 0.85 0.85 designed to be connected to each other according to the order. The generating template. Each system action tag has at least one database from the dialogue corpus. The dialogue manager User Satisfaction 0.75 0.73 overall system aims to output the synthesized spoken response utterance generating template which is constructed manually. TCR: User Perception of Task Completion Rate retrieves the best dialogue example for the current dialogue corresponding to an input utterance spoken by the user.. The system utterance generating task is advanced by filling STR: Success Turn Rate situation, which includes a current user utterance, semantic slots in the template with proper values, such as retrieving MRA: Mean Recognition Accuracy frame and discourse history. From the retrieved result, the User Satisfaction = aTCR + bSTR + rMRA results from the EPG database, slot values in the semantic dialogue manager determines the system action tag from the User Utterance ASR Language frame, and constituents in the discourse history. MODEL pre-defined tag set. IMPLEMENTATION NLU SLU MODEL WEB Dialogue System Action Tag Inform_Channel Semantic Meta-Rules Frame User’s Utterance Corpus [program_name]은 [channel]에서 합니다. For DM Dialogue Automatic Utterance Template ( [program_name] eun [channel] e-seo hap-ni-da ) Manager User Semantic Discourse Indexing [program_name] is broadcasted on [channel]. Dialogue Intention Frame History Example DB System System Domain Slot Values [program_name = 해신, channel = KBS] Action Expert EPG DB Responses Dialogue Query Generation Meta-Rules System Response EPG DB Manager Example DB 해신은 KBS에서 합니다. For SRG Generator System Utterance ( Hae-Sin eun KBS e-seo hap-ni-da ) Utterance Similarity Retrieval Hae-Sin is broadcasted on KBS.  Lexico-semantic Similarity  Discourse history Similarity Best Dialogue Dialogue TTS System Utterance Examples Example Tie-breaking Overview of POSSDS-EPG System Architecture