Impact of Ontology Complexity on Robustness of Synthesized Training Data for Cross-Lingual Dialogue State Transfer

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

What is the impact of ontology complexity on the robustness of synthesized training data for cross-lingual dialogue state transfer in multi-domain settings?

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Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

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