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

Abstract
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 low-resource languages. To tackle this challenge, we first use a set of very few parallel word pairs to refine the aligned cross-lingual word-level representations. We then employ a latent variable model to cope with the variance of similar sentences 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.
Anthology ID:
D19-1129
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1297–1303
Language:
URL:
https://aclanthology.org/D19-1129
DOI:
10.18653/v1/D19-1129
Bibkey:
Cite (ACL):
Zihan Liu, Jamin Shin, Yan Xu, Genta Indra Winata, Peng Xu, Andrea Madotto, and Pascale Fung. 2019. Zero-shot Cross-lingual Dialogue Systems with Transferable Latent Variables. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 1297–1303, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Zero-shot Cross-lingual Dialogue Systems with Transferable Latent Variables (Liu et al., EMNLP-IJCNLP 2019)
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https://preview.aclanthology.org/ingestion-script-update/D19-1129.pdf
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