  Sequence-to-Sequence (Seq2Seq) models have achieved encouraging performance
on the dialogue response generation task. However, existing Seq2Seq-based
response generation methods suffer from a low-diversity problem: they
frequently generate generic responses, which make the conversation less
interesting. In this paper, we address the low-diversity problem by
investigating its connection with model over-confidence reflected in predicted
distributions. Specifically, we first analyze the influence of the commonly
used Cross-Entropy (CE) loss function, and find that the CE loss function
prefers high-frequency tokens, which results in low-diversity responses. We
then propose a Frequency-Aware Cross-Entropy (FACE) loss function that improves
over the CE loss function by incorporating a weighting mechanism conditioned on
token frequency. Extensive experiments on benchmark datasets show that the FACE
loss function is able to substantially improve the diversity of existing
state-of-the-art Seq2Seq response generation methods, in terms of both
automatic and human evaluations.
