Taming Continuous Posteriors for Latent Variational Dialogue Policies
This work addresses performance bottlenecks in dialogue systems for users, though it is incremental by building on existing latent-action RL methods.
The paper tackled the problem of improving dialogue success in task-oriented dialogue systems by revisiting Gaussian variational posteriors for latent-action reinforcement learning, achieving state-of-the-art success rates on the MultiWOZ benchmark with competitive response coherence.
Utilizing amortized variational inference for latent-action reinforcement learning (RL) has been shown to be an effective approach in Task-oriented Dialogue (ToD) systems for optimizing dialogue success. Until now, categorical posteriors have been argued to be one of the main drivers of performance. In this work we revisit Gaussian variational posteriors for latent-action RL and show that they can yield even better performance than categoricals. We achieve this by simplifying the training procedure and propose ways to regularize the latent dialogue policy to retain good response coherence. Using continuous latent representations our model achieves state of the art dialogue success rate on the MultiWOZ benchmark, and also compares well to categorical latent methods in response coherence.