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A million tweets are worth a few points : tuning transformers for customer service tasks

Amir Hadifar (UGent) , Sofie Labat (UGent) , Veronique Hoste (UGent) , Chris Develder (UGent) and Thomas Demeester (UGent)
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Abstract
In online domain-specific customer service applications, many companies struggle to deploy advanced NLP models successfully, due to the limited availability of and noise in their datasets. While prior research demonstrated the potential of migrating large open-domain pretrained models for domain-specific tasks, the appropriate (pre)training strategies have not yet been rigorously evaluated in such social media customer service settings, especially under multilingual conditions. We address this gap by collecting a multilingual social media corpus containing customer service conversations (865k tweets), comparing various pipelines of pretraining and finetuning approaches, applying them on 5 different end tasks. We show that pretraining a generic multilingual transformer model on our in-domain dataset, before finetuning on specific end tasks, consistently boosts performance, especially in non-English settings.

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MLA
Hadifar, Amir, et al. “A Million Tweets Are Worth a Few Points : Tuning Transformers for Customer Service Tasks.” Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics : Human Language Technologies, edited by Kristina Toutanova et al., Association for Computational Linguistics (ACL), 2021, pp. 220–25, doi:10.18653/v1/2021.naacl-main.21.
APA
Hadifar, A., Labat, S., Hoste, V., Develder, C., & Demeester, T. (2021). A million tweets are worth a few points : tuning transformers for customer service tasks. In K. Toutanova, A. Rumshisky, L. Zettlemoyer, D. Hakkani-Tur, I. Beltagy, S. Bethard, … Y. Zhou (Eds.), Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics : Human Language Technologies (pp. 220–225). https://doi.org/10.18653/v1/2021.naacl-main.21
Chicago author-date
Hadifar, Amir, Sofie Labat, Veronique Hoste, Chris Develder, and Thomas Demeester. 2021. “A Million Tweets Are Worth a Few Points : Tuning Transformers for Customer Service Tasks.” In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics : Human Language Technologies, edited by Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, and Yichao Zhou, 220–25. Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2021.naacl-main.21.
Chicago author-date (all authors)
Hadifar, Amir, Sofie Labat, Veronique Hoste, Chris Develder, and Thomas Demeester. 2021. “A Million Tweets Are Worth a Few Points : Tuning Transformers for Customer Service Tasks.” In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics : Human Language Technologies, ed by. Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, and Yichao Zhou, 220–225. Association for Computational Linguistics (ACL). doi:10.18653/v1/2021.naacl-main.21.
Vancouver
1.
Hadifar A, Labat S, Hoste V, Develder C, Demeester T. A million tweets are worth a few points : tuning transformers for customer service tasks. In: Toutanova K, Rumshisky A, Zettlemoyer L, Hakkani-Tur D, Beltagy I, Bethard S, et al., editors. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics : Human Language Technologies. Association for Computational Linguistics (ACL); 2021. p. 220–5.
IEEE
[1]
A. Hadifar, S. Labat, V. Hoste, C. Develder, and T. Demeester, “A million tweets are worth a few points : tuning transformers for customer service tasks,” in Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics : Human Language Technologies, Online, 2021, pp. 220–225.
@inproceedings{8708982,
  abstract     = {{In online domain-specific customer service applications, many companies struggle to deploy advanced NLP models successfully, due to the limited availability of and noise in their datasets. While prior research demonstrated the potential of migrating large open-domain pretrained models for domain-specific tasks, the appropriate (pre)training strategies have not yet been rigorously evaluated in such social media customer service settings, especially under multilingual conditions. We address this gap by collecting a multilingual social media corpus containing customer service conversations (865k tweets), comparing various pipelines of pretraining and finetuning approaches, applying them on 5 different end tasks. We show that pretraining a generic multilingual transformer model on our in-domain dataset, before finetuning on specific end tasks, consistently boosts performance, especially in non-English settings.}},
  author       = {{Hadifar, Amir and Labat, Sofie and Hoste, Veronique and Develder, Chris and Demeester, Thomas}},
  booktitle    = {{Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics : Human Language Technologies}},
  editor       = {{Toutanova, Kristina and Rumshisky, Anna and Zettlemoyer, Luke and Hakkani-Tur, Dilek and Beltagy, Iz and Bethard, Steven and Cotterell, Ryan and Chakraborty, Tanmoy and Zhou, Yichao}},
  isbn         = {{9781954085466}},
  language     = {{eng}},
  location     = {{Online}},
  pages        = {{220--225}},
  publisher    = {{Association for Computational Linguistics (ACL)}},
  title        = {{A million tweets are worth a few points : tuning transformers for customer service tasks}},
  url          = {{http://dx.doi.org/10.18653/v1/2021.naacl-main.21}},
  year         = {{2021}},
}

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