XQA-DST: Multi-Domain and Multi-Lingual Dialogue State Tracking


In a task-oriented dialogue system, Dialogue State Tracking (DST) keeps track of all important information by filling slots with values given through the conversation. Existing methods generally rely on a predefined set of values and struggle to generalise to previously unseen slots in new domains. In this paper, we propose a multi-domain and multi-lingual dialogue state tracker in a neural reading comprehension approach. Our approach fills the slot values using span prediction, where the values are extracted from the dialogue itself. With a novel training strategy and an independent domain classifier, empirical results demonstrate that our model is a domain-scalable and open-vocabulary model that achieves 53.2% Joint Goal Accuracy (JGA) on MultiWOZ 2.1. We show its competitive transferability by zero-shot domain-adaptation experiments on MultiWOZ 2.1 with an average JGA of 31.6% for five domains. In addition, it achieves cross-lingual transfer with state-of-the-art zero-shot results, 64.9% JGA from English to German and 68.6% JGA from English to Italian on WOZ 2.0.

Findings of the 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL), 2023
Han Zhou
Han Zhou
PhD student in NLP