@inproceedings{yuan-chen-2023-lazybob,
title = "Lazybob at {S}em{E}val-2023 Task 9: Quantifying Intimacy of Multilingual Tweets with Multi-Task Learning",
author = "Yuan, Mengfei and
Chen, Cheng",
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-1.128",
doi = "10.18653/v1/2023.semeval-1.128",
pages = "927--933",
abstract = "This study presents a systematic method for analyzing the level of intimacy in tweets across ten different languages, using multi-task learning for SemEval 2023 Task 9: Multilingual Tweet Intimacy Analysis. The system begins with the utilization of the official training data, and then we experiment with different fine-tuning tricks and effective strategies, such as data augmentation, multi-task learning, etc. Through additional experiments, the approach is shown to be effective for the task. To enhance the model{'}s robustness, different transformer-based language models and some widely-used plug-and-play priors are incorporated into our system. Our final submission achieved a Pearson R of 0.6160 for the intimacy score on the official test set, placing us at the top of the leader board among 45 teams.",
}
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<abstract>This study presents a systematic method for analyzing the level of intimacy in tweets across ten different languages, using multi-task learning for SemEval 2023 Task 9: Multilingual Tweet Intimacy Analysis. The system begins with the utilization of the official training data, and then we experiment with different fine-tuning tricks and effective strategies, such as data augmentation, multi-task learning, etc. Through additional experiments, the approach is shown to be effective for the task. To enhance the model’s robustness, different transformer-based language models and some widely-used plug-and-play priors are incorporated into our system. Our final submission achieved a Pearson R of 0.6160 for the intimacy score on the official test set, placing us at the top of the leader board among 45 teams.</abstract>
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%0 Conference Proceedings
%T Lazybob at SemEval-2023 Task 9: Quantifying Intimacy of Multilingual Tweets with Multi-Task Learning
%A Yuan, Mengfei
%A Chen, Cheng
%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 yuan-chen-2023-lazybob
%X This study presents a systematic method for analyzing the level of intimacy in tweets across ten different languages, using multi-task learning for SemEval 2023 Task 9: Multilingual Tweet Intimacy Analysis. The system begins with the utilization of the official training data, and then we experiment with different fine-tuning tricks and effective strategies, such as data augmentation, multi-task learning, etc. Through additional experiments, the approach is shown to be effective for the task. To enhance the model’s robustness, different transformer-based language models and some widely-used plug-and-play priors are incorporated into our system. Our final submission achieved a Pearson R of 0.6160 for the intimacy score on the official test set, placing us at the top of the leader board among 45 teams.
%R 10.18653/v1/2023.semeval-1.128
%U https://aclanthology.org/2023.semeval-1.128
%U https://doi.org/10.18653/v1/2023.semeval-1.128
%P 927-933
Markdown (Informal)
[Lazybob at SemEval-2023 Task 9: Quantifying Intimacy of Multilingual Tweets with Multi-Task Learning](https://aclanthology.org/2023.semeval-1.128) (Yuan & Chen, SemEval 2023)
ACL