Attention-Informed Mixed-Language Training for Zero-shot Cross-lingual Task-oriented Dialogue Systems
This work addresses the problem of data scarcity for low-resource language dialogue systems, offering a more efficient alternative to large bilingual data methods, though it is incremental in leveraging existing attention mechanisms and dictionaries.
The paper tackles the challenge of developing task-oriented dialogue systems for low-resource languages by introducing Attention-Informed Mixed-Language Training, a zero-shot adaptation method that uses very few parallel word pairs to generate code-switching sentences, achieving significant performance improvements in cross-lingual dialogue state tracking and natural language understanding tasks compared to state-of-the-art approaches.
Recently, data-driven task-oriented dialogue systems have achieved promising performance in English. However, developing dialogue systems that support low-resource languages remains a long-standing challenge due to the absence of high-quality data. In order to circumvent the expensive and time-consuming data collection, we introduce Attention-Informed Mixed-Language Training (MLT), a novel zero-shot adaptation method for cross-lingual task-oriented dialogue systems. It leverages very few task-related parallel word pairs to generate code-switching sentences for learning the inter-lingual semantics across languages. Instead of manually selecting the word pairs, we propose to extract source words based on the scores computed by the attention layer of a trained English task-related model and then generate word pairs using existing bilingual dictionaries. Furthermore, intensive experiments with different cross-lingual embeddings demonstrate the effectiveness of our approach. Finally, with very few word pairs, our model achieves significant zero-shot adaptation performance improvements in both cross-lingual dialogue state tracking and natural language understanding (i.e., intent detection and slot filling) tasks compared to the current state-of-the-art approaches, which utilize a much larger amount of bilingual data.