Incorporating Interlocutor-Aware Context into Response Generation on Multi-Party Chatbots

Cao Liu, Kang Liu, Shizhu He, Zaiqing Nie, Jun Zhao


Abstract
Conventional chatbots focus on two-party response generation, which simplifies the real dialogue scene. In this paper, we strive toward a novel task of Response Generation on Multi-Party Chatbot (RGMPC), where the generated responses heavily rely on the interlocutors’ roles (e.g., speaker and addressee) and their utterances. Unfortunately, complex interactions among the interlocutors’ roles make it challenging to precisely capture conversational contexts and interlocutors’ information. Facing this challenge, we present a response generation model which incorporates Interlocutor-aware Contexts into Recurrent Encoder-Decoder frameworks (ICRED) for RGMPC. Specifically, we employ interactive representations to capture dialogue contexts for different interlocutors. Moreover, we leverage an addressee memory to enhance contextual interlocutor information for the target addressee. Finally, we construct a corpus for RGMPC based on an existing open-access dataset. Automatic and manual evaluations demonstrate that the ICRED remarkably outperforms strong baselines.
Anthology ID:
K19-1067
Volume:
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venue:
CoNLL
SIG:
SIGNLL
Publisher:
Association for Computational Linguistics
Note:
Pages:
718–727
Language:
URL:
https://www.aclweb.org/anthology/K19-1067
DOI:
10.18653/v1/K19-1067
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PDF:
http://aclanthology.lst.uni-saarland.de/K19-1067.pdf