Counterfactual Off-Policy Training for Neural Response Generation
This work addresses the challenge of generating diverse and high-quality responses in dialogue systems, though it is incremental as it builds on existing adversarial learning frameworks.
The paper tackles the data insufficiency problem in open-domain dialogue generation by using counterfactual reasoning to infer higher-quality alternative responses, and shows significant performance improvements over baseline models on the DailyDialog dataset.
Open-domain dialogue generation suffers from the data insufficiency problem due to the vast size of potential responses. In this paper, we propose to explore potential responses by counterfactual reasoning. Given an observed response, the counterfactual reasoning model automatically infers the outcome of an alternative policy that could have been taken. The resulting counterfactual response synthesized in hindsight is of higher quality than the response synthesized from scratch. Training on the counterfactual responses under the adversarial learning framework helps to explore the high-reward area of the potential response space. An empirical study on the DailyDialog dataset shows that our approach significantly outperforms the HRED model as well as the conventional adversarial learning approaches.