Lazybob at SemEval-2023 Task 9: Quantifying Intimacy of Multilingual Tweets with Multi-Task Learning
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.- Anthology ID:
- 2023.semeval-1.128
- Volume:
- Proceedings of the The 17th International Workshop on Semantic Evaluation (SemEval-2023)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 927–933
- Language:
- URL:
- https://aclanthology.org/2023.semeval-1.128
- DOI:
- Cite (ACL):
- Mengfei Yuan and Cheng Chen. 2023. Lazybob at SemEval-2023 Task 9: Quantifying Intimacy of Multilingual Tweets with Multi-Task Learning. In Proceedings of the The 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 927–933, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Lazybob at SemEval-2023 Task 9: Quantifying Intimacy of Multilingual Tweets with Multi-Task Learning (Yuan & Chen, SemEval 2023)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2023.semeval-1.128.pdf