Mimic and Rephrase: Reflective Listening in Open-Ended Dialogue

Justin Dieter, Tian Wang, Arun Tejasvi Chaganty, Gabor Angeli, Angel X. Chang


Abstract
Reflective listening–demonstrating that you have heard your conversational partner–is key to effective communication. Expert human communicators often mimic and rephrase their conversational partner, e.g., when responding to sentimental stories or to questions they don’t know the answer to. We introduce a new task and an associated dataset wherein dialogue agents similarly mimic and rephrase a user’s request to communicate sympathy (I’m sorry to hear that) or lack of knowledge (I do not know that). We study what makes a rephrasal response good against a set of qualitative metrics. We then evaluate three models for generating responses: a syntax-aware rule-based system, a seq2seq LSTM neural models with attention (S2SA), and the same neural model augmented with a copy mechanism (S2SA+C). In a human evaluation, we find that S2SA+C and the rule-based system are comparable and approach human-generated response quality. In addition, experiences with a live deployment of S2SA+C in a customer support setting suggest that this generation task is a practical contribution to real world conversational agents.
Anthology ID:
K19-1037
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:
393–403
Language:
URL:
https://www.aclweb.org/anthology/K19-1037
DOI:
10.18653/v1/K19-1037
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http://aclanthology.lst.uni-saarland.de/K19-1037.pdf
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