NLP-LISAC at SemEval-2023 Task 9: Multilingual Tweet Intimacy Analysis via a Transformer-based Approach and Data Augmentation

Abdessamad Benlahbib, Hamza Alami, Achraf Boumhidi, Omar Benslimane


Abstract
This paper presents our system and findings for SemEval 2023 Task 9 Tweet Intimacy Analysis. The main objective of this task was to predict the intimacy of tweets in 10 languages. Our submitted model (ranked 28/45) consists of a transformer-based approach with data augmentation via machine translation.
Anthology ID:
2023.semeval-1.16
Volume:
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Atul Kr. Ojha, A. Seza Doğruöz, Giovanni Da San Martino, Harish Tayyar Madabushi, Ritesh Kumar, Elisa Sartori
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
121–124
Language:
URL:
https://aclanthology.org/2023.semeval-1.16
DOI:
10.18653/v1/2023.semeval-1.16
Bibkey:
Cite (ACL):
Abdessamad Benlahbib, Hamza Alami, Achraf Boumhidi, and Omar Benslimane. 2023. NLP-LISAC at SemEval-2023 Task 9: Multilingual Tweet Intimacy Analysis via a Transformer-based Approach and Data Augmentation. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 121–124, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
NLP-LISAC at SemEval-2023 Task 9: Multilingual Tweet Intimacy Analysis via a Transformer-based Approach and Data Augmentation (Benlahbib et al., SemEval 2023)
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PDF:
https://aclanthology.org/2023.semeval-1.16.pdf