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An Empirical Study of Cross-Lingual Transferability in Generative Dialogue State Tracker

2021-01-27 12:45:55
Yen-Ting Lin, Yun-Nung Chen
arXiv_CL
           
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 pre-trained seq2seq model. We experiment under different settings, including joint-training or pre-training on cross-lingual and cross-ontology datasets. We also find out the low cross-lingual transferability of our approaches and provides investigation and discussion.

Abstract (translated)

URL

https://arxiv.org/abs/2101.11360

PDF

https://arxiv.org/pdf/2101.11360.pdf

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