Mimic and Rephrase: Reflective Listening in Open-Ended Dialogue
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.
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