Scaling Training Data and Cross-Lingual Transferability in Low-Resource Dialogue State Tracking

Abstract

Abstract: There has been a rapid development in data-driven task-oriented dialogue systems with the benefit of large-scale datasets. However, the progress of dialogue systems in low-resource languages lags far behind due to the lack of high-quality data. To advance the cross-lingual technology in building dialog systems, DSTC9 introduces the task of cross-lingual dialog state tracking, where we test the DST module in a low-resource language given the rich-resource training dataset. This paper studies the transferability of a cross-lingual generative dialogue state tracking system using a multilingual

Research Question

How does the scaling of training data size affect the cross-lingual transferability of dialogue state trackers in low-resource languages, as measured by model performance on standard benchmarks like MultiWOZ?

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