Self-supervised attention flow for dialogue state tracking

The performance of existing approaches for dialogue state tracking (DST) is often limited by the deficiency of labeled datasets, and inefficient utilization of data is also a practical yet tough problem of the DST task. In this paper, we aim to tackle these challenges in a self-supervised manner by introducing an auxiliary pre-training task that learns to pick up the correct dialogue response from a group of candidates. Moreover, we propose an attention flow mechanism that is augmented with a soft-threshold function in a dynamic way to better understand the user intent and filter out the redundant information. Extensive experiments on the multi-domain dialogue state tracking dataset MultiWOZ 2.1 demonstrate the effectiveness of our proposed method, and we also show that it is able to adapt to zero/few-shot cases under the proposed self-supervised framework.