  Existing conversational systems tend to generate generic responses. Recently,
Background Based Conversations (BBCs) have been introduced to address this
issue. Here, the generated responses are grounded in some background
information. The proposed methods for BBCs are able to generate more
informative responses, they either cannot generate natural responses or have
difficulty in locating the right background information. In this paper, we
propose a Reference-aware Network (RefNet) to address the two issues. Unlike
existing methods that generate responses token by token, RefNet incorporates a
novel reference decoder that provides an alternative way to learn to directly
cite a semantic unit (e.g., a span containing complete semantic information)
from the background. Experimental results show that RefNet significantly
outperforms state-of-the-art methods in terms of both automatic and human
evaluations, indicating that RefNet can generate more appropriate and
human-like responses.
