@inproceedings{jarnfors-etal-2021-using,
title = "Using {BERT} for choosing classifiers in {M}andarin",
author = {J{\"a}rnfors, Jani and
Chen, Guanyi and
van Deemter, Kees and
Sybesma, Rint},
editor = "Belz, Anya and
Fan, Angela and
Reiter, Ehud and
Sripada, Yaji",
booktitle = "Proceedings of the 14th International Conference on Natural Language Generation",
month = aug,
year = "2021",
address = "Aberdeen, Scotland, UK",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.inlg-1.17",
doi = "10.18653/v1/2021.inlg-1.17",
pages = "172--176",
abstract = "Choosing the most suitable classifier in a linguistic context is a well-known problem in the production of Mandarin and many other languages. The present paper proposes a solution based on BERT, compares this solution to previous neural and rule-based models, and argues that the BERT model performs particularly well on those difficult cases where the classifier adds information to the text.",
}
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<abstract>Choosing the most suitable classifier in a linguistic context is a well-known problem in the production of Mandarin and many other languages. The present paper proposes a solution based on BERT, compares this solution to previous neural and rule-based models, and argues that the BERT model performs particularly well on those difficult cases where the classifier adds information to the text.</abstract>
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%0 Conference Proceedings
%T Using BERT for choosing classifiers in Mandarin
%A Järnfors, Jani
%A Chen, Guanyi
%A van Deemter, Kees
%A Sybesma, Rint
%Y Belz, Anya
%Y Fan, Angela
%Y Reiter, Ehud
%Y Sripada, Yaji
%S Proceedings of the 14th International Conference on Natural Language Generation
%D 2021
%8 August
%I Association for Computational Linguistics
%C Aberdeen, Scotland, UK
%F jarnfors-etal-2021-using
%X Choosing the most suitable classifier in a linguistic context is a well-known problem in the production of Mandarin and many other languages. The present paper proposes a solution based on BERT, compares this solution to previous neural and rule-based models, and argues that the BERT model performs particularly well on those difficult cases where the classifier adds information to the text.
%R 10.18653/v1/2021.inlg-1.17
%U https://aclanthology.org/2021.inlg-1.17
%U https://doi.org/10.18653/v1/2021.inlg-1.17
%P 172-176
Markdown (Informal)
[Using BERT for choosing classifiers in Mandarin](https://aclanthology.org/2021.inlg-1.17) (Järnfors et al., INLG 2021)
ACL
- Jani Järnfors, Guanyi Chen, Kees van Deemter, and Rint Sybesma. 2021. Using BERT for choosing classifiers in Mandarin. In Proceedings of the 14th International Conference on Natural Language Generation, pages 172–176, Aberdeen, Scotland, UK. Association for Computational Linguistics.