@inproceedings{zhang-etal-2023-zbl2w,
title = "{ZBL}2{W} at {S}em{E}val-2023 Task 9: A Multilingual Fine-tuning Model with Data Augmentation for Tweet Intimacy Analysis",
author = "Zhang, Hao and
Wu, Youlin and
Lu, Junyu and
Bai, Zewen and
Wu, Jiangming and
Lin, Hongfei and
Zhang, Shaowu",
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-1.106",
doi = "10.18653/v1/2023.semeval-1.106",
pages = "770--775",
abstract = "This paper describes our system used in the SemEval-2023 Task 9 Multilingual Tweet Intimacy Analysis. There are two key challenges in this task: the complexity of multilingual and zero-shot cross-lingual learning, and the difficulty of semantic mining of tweet intimacy. To solve the above problems, our system extracts contextual representations from the pretrained language models, XLM-T, and employs various optimization methods, including adversarial training, data augmentation, ordinal regression loss and special training strategy. Our system ranked 14th out of 54 participating teams on the leaderboard and ranked 10th on predicting languages not in the training data. Our code is available on Github.",
}
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<abstract>This paper describes our system used in the SemEval-2023 Task 9 Multilingual Tweet Intimacy Analysis. There are two key challenges in this task: the complexity of multilingual and zero-shot cross-lingual learning, and the difficulty of semantic mining of tweet intimacy. To solve the above problems, our system extracts contextual representations from the pretrained language models, XLM-T, and employs various optimization methods, including adversarial training, data augmentation, ordinal regression loss and special training strategy. Our system ranked 14th out of 54 participating teams on the leaderboard and ranked 10th on predicting languages not in the training data. Our code is available on Github.</abstract>
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%0 Conference Proceedings
%T ZBL2W at SemEval-2023 Task 9: A Multilingual Fine-tuning Model with Data Augmentation for Tweet Intimacy Analysis
%A Zhang, Hao
%A Wu, Youlin
%A Lu, Junyu
%A Bai, Zewen
%A Wu, Jiangming
%A Lin, Hongfei
%A Zhang, Shaowu
%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 zhang-etal-2023-zbl2w
%X This paper describes our system used in the SemEval-2023 Task 9 Multilingual Tweet Intimacy Analysis. There are two key challenges in this task: the complexity of multilingual and zero-shot cross-lingual learning, and the difficulty of semantic mining of tweet intimacy. To solve the above problems, our system extracts contextual representations from the pretrained language models, XLM-T, and employs various optimization methods, including adversarial training, data augmentation, ordinal regression loss and special training strategy. Our system ranked 14th out of 54 participating teams on the leaderboard and ranked 10th on predicting languages not in the training data. Our code is available on Github.
%R 10.18653/v1/2023.semeval-1.106
%U https://aclanthology.org/2023.semeval-1.106
%U https://doi.org/10.18653/v1/2023.semeval-1.106
%P 770-775
Markdown (Informal)
[ZBL2W at SemEval-2023 Task 9: A Multilingual Fine-tuning Model with Data Augmentation for Tweet Intimacy Analysis](https://aclanthology.org/2023.semeval-1.106) (Zhang et al., SemEval 2023)
ACL