  Since the pre-trained language models are widely used, retrieval-based
open-domain dialog systems, have attracted considerable attention from
researchers recently. Most of the previous works select a suitable response
only according to the matching degree between the query and each individual
candidate response. Although good performance has been achieved, these recent
works ignore the comparison among the candidate responses, which could provide
rich information for selecting the most appropriate response. Intuitively,
better decisions could be made when the models can get access to the comparison
information among all the candidate responses. In order to leverage the
comparison information among the candidate responses, in this paper, we propose
a novel and plug-in Self-attention Comparison Module for retrieval-based
open-domain dialog systems, called SCM. Extensive experiment results
demonstrate that our proposed self-attention comparison module effectively
boosts the performance of the existing retrieval-based open-domain dialog
systems. Besides, we have publicly released our source codes for future
research.
