Are Training Samples Correlated? Learning to Generate Dialogue
Responses with Multiple References
Abstract
Due to its potential applications, open-domain
dialogue generation has become popular and
achieved remarkable progress in recent years,
but sometimes suffers from generic responses.
Previous models are generally trained based
on 1-to-1 mapping from an input query to its
response, which actually ignores the nature
of 1-to-n mapping in dialogue that there may
exist multiple valid responses corresponding
to the same query. In this paper, we propose to utilize the multiple references by considering the correlation of different valid responses and modeling the 1-to-n mapping with
a novel two-step generation architecture. The
first generation phase extracts the common
features of different responses which, combined with distinctive features obtained in the
second phase, can generate multiple diverse
and appropriate responses. Experimental results show that our proposed model can effectively improve the quality of response and
outperform existing neural dialogue models on
both automatic and human evaluations