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

• URL: http://arxiv.org/abs/2101.11360v1
• Date: Wed, 27 Jan 2021 12:45:55 GMT
• Title: An Empirical Study of Cross-Lingual Transferability in Generative Dialogue State Tracker
• Authors: Yen-Ting Lin, Yun-Nung Chen
• Abstract summary: We study the transferability of a cross-lingual generative dialogue state tracking system using a multilingual pre-trained seq2seq model. We also find out the low cross-lingual transferability of our approaches and provides investigation and discussion.
• Score: 33.2309643963072
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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