Precognition in Task-oriented Dialogue Understanding: Posterior Regularization by Future Context
This work addresses a bottleneck in task-oriented dialogue systems by improving understanding through future context modeling, though it is incremental as it builds on existing posterior regularization techniques.
The paper tackles the problem of modeling future contexts in task-oriented dialogue understanding, which is typically not visible in real-world scenarios, by proposing a posterior regularization method that jointly models historical and future information during training, achieving superior results on two dialogue datasets.
Task-oriented dialogue systems have become overwhelmingly popular in recent researches. Dialogue understanding is widely used to comprehend users' intent, emotion and dialogue state in task-oriented dialogue systems. Most previous works on such discriminative tasks only models current query or historical conversations. Even if in some work the entire dialogue flow was modeled, it is not suitable for the real-world task-oriented conversations as the future contexts are not visible in such cases. In this paper, we propose to jointly model historical and future information through the posterior regularization method. More specifically, by modeling the current utterance and past contexts as prior, and the entire dialogue flow as posterior, we optimize the KL distance between these distributions to regularize our model during training. And only historical information is used for inference. Extensive experiments on two dialogue datasets validate the effectiveness of our proposed method, achieving superior results compared with all baseline models.