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    <abstract>This paper describes our system (iREL) for Tweet intimacy analysis sharedtask of the SemEval 2023 workshop at ACL 2023. Oursystem achieved an overall Pearson’s r score of 0.5924 and ranked 10th on the overall leaderboard. For the unseen languages, we ranked third on the leaderboard and achieved a Pearson’s r score of 0.485. We used a single multilingual model for all languages, as discussed in this paper. We provide a detailed description of our pipeline along with multiple ablation experiments to further analyse each component of the pipeline. We demonstrate how translation-based augmentation, domain-specific features, and domain-adapted pre-trained models improve the understanding of intimacy in tweets. The codecan be found at \textbackslashhrefhttps://github.com/bhavyajeet/Multilingual-tweet-intimacyhttps://github.com/bhavyajeet/Multilingual-tweet-intimacy</abstract>
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%0 Conference Proceedings
%T iREL at SemEval-2023 Task 9: Improving understanding of multilingual Tweets using Translation-Based Augmentation and Domain Adapted Pre-Trained Models
%A Singh, Bhavyajeet
%A Maity, Ankita
%A Kandru, Pavan
%A Hari, Aditya
%A Varma, Vasudeva
%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 singh-etal-2023-irel
%X This paper describes our system (iREL) for Tweet intimacy analysis sharedtask of the SemEval 2023 workshop at ACL 2023. Oursystem achieved an overall Pearson’s r score of 0.5924 and ranked 10th on the overall leaderboard. For the unseen languages, we ranked third on the leaderboard and achieved a Pearson’s r score of 0.485. We used a single multilingual model for all languages, as discussed in this paper. We provide a detailed description of our pipeline along with multiple ablation experiments to further analyse each component of the pipeline. We demonstrate how translation-based augmentation, domain-specific features, and domain-adapted pre-trained models improve the understanding of intimacy in tweets. The codecan be found at \textbackslashhrefhttps://github.com/bhavyajeet/Multilingual-tweet-intimacyhttps://github.com/bhavyajeet/Multilingual-tweet-intimacy
%R 10.18653/v1/2023.semeval-1.282
%U https://aclanthology.org/2023.semeval-1.282
%U https://doi.org/10.18653/v1/2023.semeval-1.282
%P 2052-2057

Markdown (Informal)

[iREL at SemEval-2023 Task 9: Improving understanding of multilingual Tweets using Translation-Based Augmentation and Domain Adapted Pre-Trained Models](https://aclanthology.org/2023.semeval-1.282) (Singh et al., SemEval 2023)

• iREL at SemEval-2023 Task 9: Improving understanding of multilingual Tweets using Translation-Based Augmentation and Domain Adapted Pre-Trained Models (Singh et al., SemEval 2023)

ACL

• Bhavyajeet Singh, Ankita Maity, Pavan Kandru, Aditya Hari, and Vasudeva Varma. 2023. iREL at SemEval-2023 Task 9: Improving understanding of multilingual Tweets using Translation-Based Augmentation and Domain Adapted Pre-Trained Models. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 2052–2057, Toronto, Canada. Association for Computational Linguistics.