ROZAM at SemEval 2023 Task 9: Multilingual Tweet Intimacy Analysis
Mohammadmostafa Rostamkhani, Ghazal Zamaninejad, Sauleh Eetemadi
Abstract
We build a model using large multilingual pretrained language model XLM-T for regression task and fine-tune it on the MINT (Multilingual INTmacy) analysis dataset which covers 6 languages for training and 4 languages for testing zero-shot performance of the model. The dataset was annotated and the annotations are intimacy scores. We experiment with several deep learning architectures to predict intimacy score. To achieve optimal performance we modify several model settings including loss function, number and type of layers. In total, we ran 16 end-to-end experiments. Our best system achieved a Pearson Correlation score of 0.52.- Anthology ID:
- 2023.semeval-1.278
- 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:
- 2029–2032
- Language:
- URL:
- https://aclanthology.org/2023.semeval-1.278
- DOI:
- 10.18653/v1/2023.semeval-1.278
- Cite (ACL):
- Mohammadmostafa Rostamkhani, Ghazal Zamaninejad, and Sauleh Eetemadi. 2023. ROZAM at SemEval 2023 Task 9: Multilingual Tweet Intimacy Analysis. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 2029–2032, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- ROZAM at SemEval 2023 Task 9: Multilingual Tweet Intimacy Analysis (Rostamkhani et al., SemEval 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2023.semeval-1.278.pdf