Lifelong and Interactive Learning of Factual Knowledge in Dialogues

Sahisnu Mazumder, Bing Liu, Shuai Wang, Nianzu Ma


Abstract
Dialogue systems are increasingly using knowledge bases (KBs) storing real-world facts to help generate quality responses. However, as the KBs are inherently incomplete and remain fixed during conversation, it limits dialogue systems’ ability to answer questions and to handle questions involving entities or relations that are not in the KB. In this paper, we make an attempt to propose an engine for Continuous and Interactive Learning of Knowledge (CILK) for dialogue systems to give them the ability to continuously and interactively learn and infer new knowledge during conversations. With more knowledge accumulated over time, they will be able to learn better and answer more questions. Our empirical evaluation shows that CILK is promising.
Anthology ID:
W19-5903
Volume:
Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue
Month:
September
Year:
2019
Address:
Stockholm, Sweden
Venue:
SIGDIAL
SIG:
SIGDIAL
Publisher:
Association for Computational Linguistics
Note:
Pages:
21–31
Language:
URL:
https://aclanthology.org/W19-5903
DOI:
10.18653/v1/W19-5903
Bibkey:
Cite (ACL):
Sahisnu Mazumder, Bing Liu, Shuai Wang, and Nianzu Ma. 2019. Lifelong and Interactive Learning of Factual Knowledge in Dialogues. In Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue, pages 21–31, Stockholm, Sweden. Association for Computational Linguistics.
Cite (Informal):
Lifelong and Interactive Learning of Factual Knowledge in Dialogues (Mazumder et al., SIGDIAL 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/author-url/W19-5903.pdf