Cross-lingual intent classification in a low resource industrial setting

Talaat Khalil, Kornel Kiełczewski, Georgios Christos Chouliaras, Amina Keldibek, Maarten Versteegh


Abstract
This paper explores different approaches to multilingual intent classification in a low resource setting. Recent advances in multilingual text representations promise cross-lingual transfer for classifiers. We investigate the potential for this transfer in an applied industrial setting and compare to multilingual classification using machine translated text. Our results show that while the recently developed methods show promise, practical application calls for a combination of techniques for useful results.
Anthology ID:
D19-1676
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
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
6419–6424
Language:
URL:
https://aclanthology.org/D19-1676
DOI:
10.18653/v1/D19-1676
Bibkey:
Cite (ACL):
Talaat Khalil, Kornel Kiełczewski, Georgios Christos Chouliaras, Amina Keldibek, and Maarten Versteegh. 2019. Cross-lingual intent classification in a low resource industrial setting. 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 6419–6424, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Cross-lingual intent classification in a low resource industrial setting (Khalil et al., EMNLP-IJCNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/D19-1676.pdf