@inproceedings{cai-etal-2023-ynu,
title = "{YNU}-{HPCC} at {S}em{E}val-2023 Task 9: Pretrained Language Model for Multilingual Tweet Intimacy Analysis",
author = "Cai, Qisheng and
Wang, Jin and
Zhang, Xuejie",
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-1.100",
doi = "10.18653/v1/2023.semeval-1.100",
pages = "733--738",
abstract = "This paper describes our fine-tuned pretrained language model for task 9 (Multilingual Tweet Intimacy Analysis, MTIA) of the SemEval 2023 competition. MTIA aims to quantitatively analyze tweets in 6 languages for intimacy, giving a score from 1 to 5. The challenge of MTIA is in semantically extracting information from code-mixed texts. To alleviate this difficulty, we suggested a solution that combines attention and memory mechanisms. The preprocessed tweets are input to the XLM-T layer to get sentence embeddings and subsequently to the bidirectional GRU layer to obtain intimacy ratings. Experimental results show an improvement in the overall performance of our model in both seen and unseen languages.",
}
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<abstract>This paper describes our fine-tuned pretrained language model for task 9 (Multilingual Tweet Intimacy Analysis, MTIA) of the SemEval 2023 competition. MTIA aims to quantitatively analyze tweets in 6 languages for intimacy, giving a score from 1 to 5. The challenge of MTIA is in semantically extracting information from code-mixed texts. To alleviate this difficulty, we suggested a solution that combines attention and memory mechanisms. The preprocessed tweets are input to the XLM-T layer to get sentence embeddings and subsequently to the bidirectional GRU layer to obtain intimacy ratings. Experimental results show an improvement in the overall performance of our model in both seen and unseen languages.</abstract>
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%0 Conference Proceedings
%T YNU-HPCC at SemEval-2023 Task 9: Pretrained Language Model for Multilingual Tweet Intimacy Analysis
%A Cai, Qisheng
%A Wang, Jin
%A Zhang, Xuejie
%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 cai-etal-2023-ynu
%X This paper describes our fine-tuned pretrained language model for task 9 (Multilingual Tweet Intimacy Analysis, MTIA) of the SemEval 2023 competition. MTIA aims to quantitatively analyze tweets in 6 languages for intimacy, giving a score from 1 to 5. The challenge of MTIA is in semantically extracting information from code-mixed texts. To alleviate this difficulty, we suggested a solution that combines attention and memory mechanisms. The preprocessed tweets are input to the XLM-T layer to get sentence embeddings and subsequently to the bidirectional GRU layer to obtain intimacy ratings. Experimental results show an improvement in the overall performance of our model in both seen and unseen languages.
%R 10.18653/v1/2023.semeval-1.100
%U https://aclanthology.org/2023.semeval-1.100
%U https://doi.org/10.18653/v1/2023.semeval-1.100
%P 733-738
Markdown (Informal)
[YNU-HPCC at SemEval-2023 Task 9: Pretrained Language Model for Multilingual Tweet Intimacy Analysis](https://aclanthology.org/2023.semeval-1.100) (Cai et al., SemEval 2023)
ACL