  Existing dialogue modeling methods have achieved promising performance on
various dialogue tasks with the aid of Transformer and the large-scale
pre-trained language models. However, some recent studies revealed that the
context representations produced by these methods suffer the problem of
anisotropy. In this paper, we find that the generated representations are also
not conversational, losing the conversation structure information during the
context modeling stage. To this end, we identify two properties in dialogue
modeling, i.e., locality and isotropy, and present a simple method for dialogue
representation calibration, namely SimDRC, to build isotropic and
conversational feature spaces. Experimental results show that our approach
significantly outperforms the current state-of-the-art models on three dialogue
tasks across the automatic and human evaluation metrics. More in-depth analyses
further confirm the effectiveness of our proposed approach.
