Dialog Generation Using Multi-Turn Reasoning Neural Networks

Xianchao Wu, Ander Martínez, Momo Klyen

Abstract
In this paper, we propose a generalizable dialog generation approach that adapts multi-turn reasoning, one recent advancement in the field of document comprehension, to generate responses (“answers”) by taking current conversation session context as a “document” and current query as a “question”. The major idea is to represent a conversation session into memories upon which attention-based memory reading mechanism can be performed multiple times, so that (1) user’s query is properly extended by contextual clues and (2) optimal responses are step-by-step generated. Considering that the speakers of one conversation are not limited to be one, we separate the single memory used for document comprehension into different groups for speaker-specific topic and opinion embedding. Namely, we utilize the queries’ memory, the responses’ memory, and their unified memory, following the time sequence of the conversation session. Experiments on Japanese 10-sentence (5-round) conversation modeling show impressive results on how multi-turn reasoning can produce more diverse and acceptable responses than state-of-the-art single-turn and non-reasoning baselines.
Anthology ID:
N18-1186
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Editors:
Marilyn Walker, Heng Ji, Amanda Stent
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2049–2059
Language:
URL:
https://aclanthology.org/N18-1186
DOI:
10.18653/v1/N18-1186
Bibkey:
Cite (ACL):
Xianchao Wu, Ander Martínez, and Momo Klyen. 2018. Dialog Generation Using Multi-Turn Reasoning Neural Networks. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 2049–2059, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Dialog Generation Using Multi-Turn Reasoning Neural Networks (Wu et al., NAACL 2018)
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PDF:
https://preview.aclanthology.org/nschneid-patch-2/N18-1186.pdf
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