CL-CSP: Contrastive Learning with Continuous Semantic Perturbations for Neural Dialogue Generation

The principal task in neural dialogue generation is to learn to generate coherent and diverse responses conditioned on dialogue history, and thus imitate the intrinsic conversational skills of real humans. Conventional training approaches for dialogue generation (e.g., Maximum Likely Estimation (MLE)) focus on capturing coherent syntactic patterns, but fall short of showcasing diversity in expression. In this paper, we utilize contrastive learning for dialogue, which extends the conventional MLE-based dialogue training with a contrastive counterpart, where dialogue generation models can be explicitly trained on multiple positive and negative pairs. However, naive contrastive learning with discrete manipulations is sub-optimal, since they tend to sample examples that can be easily distinguished. To migrate this problem, we propose a novel contrastive learning paradigm with continuous semantic perturbations (CL-CSP), which constructs positive examples within an adjacency semantic region and pushes those negative examples far away from the semantic region. Such “soft” positive and negative pairs constructed by CL-CSP guides the model to distinguish positive instances from negative ones better. We conduct extensive experiments to demonstrate that our proposed method improves the diversity of response generation on two benchmarks, showing that our proposed techniques are crucial to achieving superior performance.