Diverse dialogue generation with context dependent dynamic loss function

Abstract

Dialogue systems using deep learning have achieved generation of fluent response sentences to user utterances. Nevertheless, they tend to produce responses that are not diverse and which are less context-dependent. To address these shortcomings, we propose a new loss function, an Inverse N-gram Frequency (INF) loss, which incorporates contextual fluency and diversity at the same time by a simple formula. Our INF loss can adjust its loss dynamically by a weight using the inverse frequency of the tokens' n-gram applied to Softmax Cross-Entropy loss, so that rare tokens appear more likely while retaining the fluency of the generated sentences. We trained Transformer using English and Japanese Twitter replies as single-turn dialogues using different loss functions. Our INF loss model outperformed the baselines of SCE loss and ITF loss models in automatic evaluations such as DIST-N and ROUGE, and also achieved higher scores on our human evaluations of coherence and richness.

A Randomized Link Transformer for Diverse Open-Domain Dialogue Generation

“…Several other approaches to promoting response diversity which involve introducing an alternate loss function such as the Maximum Mutual Information (MMI) objective (Li et al, 2016a), the Inverse Token Frequency (ITF) objective (Nakamura et al, 2018), and the Inverse N-gram Frequency (INF) objective (Ueyama and Kano, 2020), require considerable additional computation steps. On the other hand, adversarial learning-based (Li et al, 2017a) and embedding augmentation-based (Cao et al, 2021) approaches require extensive modifications to the standard training process, resulting in a significant increase in training complexity.…”