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