Dialogue State Tracking (DST), a crucial component of task-oriented dialogue (ToD) systems, keeps track of all important information pertaining to dialogue history: filling slots with the most probable values throughout the conversation. Existing methods generally rely on a predefined set of values and struggle to generalise to previously unseen slots in new domains. To overcome these challenges, we propose a domain-agnostic extractive question answering (QA) approach with shared weights across domains. To disentangle the complex domain information in ToDs, we train our DST with a novel domain filtering strategy by excluding out-of-domain question samples. With an independent classifier that predicts the presence of multiple domains given the context, our model tackles DST by extracting spans in active domains. Empirical results demonstrate that our model can efficiently leverage domain-agnostic QA datasets by two-stage fine-tuning while being both domain-scalable and open vocabulary in DST. It shows strong transferability by achieving zero-shot domain-adaptation results on MultiWOZ 2.1 with an average JGA of 36.7%. It further achieves cross-lingual transfer with state-of-the-art zero-shot results, 66.2% JGA from English to German and 75.7% JGA from English to Italian on WOZ 2.0.

This paper describes the solution for the Social Media Mining for Health (SMM4H) 2022 Shared Task. We participated in Task1a., Task1b. and Task1c. To solve the problem of the presence of Twitter data, we used a pre-trained language model. We used training strategies that involved: adversarial training, head layer weighted fusion, etc., to improve the performance of the model. The experimental results show the effectiveness of our designed system. For task 1a, the system achieved an F1 score of 0.68; for task 1b Overlapping F1 score of 0.65 and a Strict F1 score of 0.49. Task 1c yields Overlapping F1 and Strict F1 scores of 0.36 and 0.30, respectively.