  In open-domain dialogue response generation, a dialogue context can be
continued with diverse responses, and the dialogue models should capture such
one-to-many relations. In this work, we first analyze the training objective of
dialogue models from the view of Kullback-Leibler divergence (KLD) and show
that the gap between the real world probability distribution and the
single-referenced data's probability distribution prevents the model from
learning the one-to-many relations efficiently. Then we explore approaches to
multi-referenced training in two aspects. Data-wise, we generate diverse pseudo
references from a powerful pretrained model to build multi-referenced data that
provides a better approximation of the real-world distribution. Model-wise, we
propose to equip variational models with an expressive prior, named linear
Gaussian model (LGM). Experimental results of automated evaluation and human
evaluation show that the methods yield significant improvements over baselines.
We will release our code and data in
https://github.com/ZHAOTING/dialog-processing.
