Improving Neural Response Diversity with Frequency-Aware Cross-Entropy Loss
This addresses the issue of uninteresting conversations in dialogue systems, though it is an incremental improvement over existing methods.
The paper tackles the low-diversity problem in Seq2Seq dialogue response generation, where models often produce generic responses, by proposing a Frequency-Aware Cross-Entropy (FACE) loss function that substantially improves diversity in benchmark datasets.
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.