State-Aware Adversarial Training for Utterance-Level Dialogue Generation

Dialogue generation is a challenging problem because it not only requires us to model the context in a conversation but also to exploit it to generate a coherent and fluent utterance. This paper, aiming for a specific topic of this field, proposes an adversarial training based framework for utterance-level dialogue generation. Technically, we train an encoder-decoder generator simultaneously with a discriminative classifier that make the utterance approximate to the state-aware inputs. Experiments on MultiWoZ 2.0 and MultiWoZ 2.1 datasets show that our method achieves advanced improvements on both automatic and human evaluations, and on the effectiveness of our framework facing low-resource. We further explore the effect of fine-grained augmentations for downstream dialogue state tracking (DST) tasks. Experimental results demonstrate the high-quality data generated by our proposed framework improves the performance over state-of-the-art models.