@inproceedings{bozdag-etal-2023-arizonans,
title = "Arizonans at {S}em{E}val-2023 Task 9: Multilingual Tweet Intimacy Analysis with {XLM}-{T}",
author = "Bozdag, Nimet Beyza and
Bilgis, Tugay and
Bethard, Steven",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Da San Martino, Giovanni and
Tayyar Madabushi, Harish and
Kumar, Ritesh and
Sartori, Elisa},
booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.semeval-1.230",
doi = "10.18653/v1/2023.semeval-1.230",
pages = "1656--1659",
abstract = "This paper presents the systems and approaches of the Arizonans team for the SemEval 2023 Task 9: Multilingual Tweet Intimacy Analysis. We finetune the Multilingual RoBERTa model trained with about 200M tweets, XLM-T. Our final model ranked 9th out of 45 overall, 13th in seen languages, and 8th in unseen languages.",
}
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<abstract>This paper presents the systems and approaches of the Arizonans team for the SemEval 2023 Task 9: Multilingual Tweet Intimacy Analysis. We finetune the Multilingual RoBERTa model trained with about 200M tweets, XLM-T. Our final model ranked 9th out of 45 overall, 13th in seen languages, and 8th in unseen languages.</abstract>
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%0 Conference Proceedings
%T Arizonans at SemEval-2023 Task 9: Multilingual Tweet Intimacy Analysis with XLM-T
%A Bozdag, Nimet Beyza
%A Bilgis, Tugay
%A Bethard, Steven
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F bozdag-etal-2023-arizonans
%X This paper presents the systems and approaches of the Arizonans team for the SemEval 2023 Task 9: Multilingual Tweet Intimacy Analysis. We finetune the Multilingual RoBERTa model trained with about 200M tweets, XLM-T. Our final model ranked 9th out of 45 overall, 13th in seen languages, and 8th in unseen languages.
%R 10.18653/v1/2023.semeval-1.230
%U https://aclanthology.org/2023.semeval-1.230
%U https://doi.org/10.18653/v1/2023.semeval-1.230
%P 1656-1659
Markdown (Informal)
[Arizonans at SemEval-2023 Task 9: Multilingual Tweet Intimacy Analysis with XLM-T](https://aclanthology.org/2023.semeval-1.230) (Bozdag et al., SemEval 2023)
ACL