Extensions to hybrid code networks for FAIR dialog dataset

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Goal-oriented dialog systems require a different approach from chit-chat conversational systems in that they should perform various subtasks as well as continue the conversation itself. Since these systems typically interact with an external knowledge base that changes over time, it is desirable to incorporate domain knowledge to deal with such changes, yet with minimum human effort. This paper presents an extended version of the Hybrid Code Network (HCN) developed for the Facebook AI research (FAIR) dialog dataset used in the Sixth Dialog System Technology Challenge (DSTC6). Compared to the original HCN, the system was more adaptable to changes in the knowledge base due to the modules that are extended to be learned from data. Using the proposed learning scheme with fairly elementary domain-specific rules, the proposed model achieved 100% accuracy in all test datasets. (C) 2018 Elsevier Ltd. All rights reserved.
Publisher
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
Issue Date
2019-01
Language
English
Article Type
Article
Keywords

SYSTEMS

Citation

COMPUTER SPEECH AND LANGUAGE, v.53, pp.80 - 91

ISSN
0885-2308
DOI
10.1016/j.csl.2018.07.004
URI
http://hdl.handle.net/10203/246201
Appears in Collection
RIMS Journal Papers
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