Source-Target Data Ratio Optimization in Multilingual Task-Oriented Dialogue Systems for Low-Resource Languages

Abstract

Abstract: Most of the current task-oriented dialogue systems (ToD), despite having interesting results, are designed for a handful of languages like Chinese and English. Therefore, their performance in low-resource languages is still a significant problem due to the absence of a standard dataset and evaluation policy. To address this problem, we proposed ViWOZ, a fully-annotated Vietnamese task-oriented dialogue dataset. ViWOZ is the first multi-turn, multi-domain tasked oriented dataset in Vietnamese, a low-resource language. The dataset consists of a total of 5,000 dialogues, including 60,946 fully an

Research Question

What is the impact of varying the ratio of source-domain (English) to target-domain (low-resource language) training data in MLT on model performance, measured by perplexity and response quality metrics (e.g., BERTScore) in cross-lingual task-oriented dialogue systems?

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