@inproceedings{khalil-etal-2019-cross,
title = "Cross-lingual intent classification in a low resource industrial setting",
author = "Khalil, Talaat and
Kie{\l}czewski, Kornel and
Chouliaras, Georgios Christos and
Keldibek, Amina and
Versteegh, Maarten",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "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 = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1676",
doi = "10.18653/v1/D19-1676",
pages = "6419--6424",
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.",
}
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%0 Conference Proceedings
%T Cross-lingual intent classification in a low resource industrial setting
%A Khalil, Talaat
%A Kiełczewski, Kornel
%A Chouliaras, Georgios Christos
%A Keldibek, Amina
%A Versteegh, Maarten
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F khalil-etal-2019-cross
%X 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.
%R 10.18653/v1/D19-1676
%U https://aclanthology.org/D19-1676
%U https://doi.org/10.18653/v1/D19-1676
%P 6419-6424
Markdown (Informal)
[Cross-lingual intent classification in a low resource industrial setting](https://aclanthology.org/D19-1676) (Khalil et al., EMNLP-IJCNLP 2019)
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.