Zero-shot Cross-lingual Dialogue Systems with Transferable Latent Variables

Zihan Liu, Jamin Shin, Yan Xu, Genta Indra Winata, Peng Xu, Andrea Madotto, Pascale Fung
2019 Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)  
Despite the surging demands for multilingual task-oriented dialog systems (e.g., Alexa, Google Home), there has been less research done in multilingual or cross-lingual scenarios. Hence, we propose a zero-shot adaptation of task-oriented dialogue system to lowresource languages. To tackle this challenge, we first use a set of very few parallel word pairs to refine the aligned cross-lingual wordlevel representations. We then employ a latent variable model to cope with the variance of similar
more » ... ences across different languages, which is induced by imperfect cross-lingual alignments and inherent differences in languages. Finally, the experimental results show that even though we utilize much less external resources, our model achieves better adaptation performance for natural language understanding task (i.e., the intent detection and slot filling) compared to the current state-of-the-art model in the zero-shot scenario.
doi:10.18653/v1/d19-1129 dblp:conf/emnlp/LiuSXWXMF19 fatcat:5l7jx52axzdoxk2csrvxn47mye