Abstract
SMM4H-2022 (CITATION) Task 2 is to detect whether containing premise in the tweets of users about COVID-19 on the social medias or their stances for the claims. In this paper, we propose Tweet Claim Matching (TCM), which is a new pre-training task constructed by the tweets and claims similarly to Next Sentence Prediction (NSP). We first continue to pre-train the standard pre-trained language models on the labelled dataset and then fine-tune them for obtaining better performance. Compared with the solid baseline (CITATION), we achieve the absolute improvement of 7.9% in Task 2a and obtain the SOTA results.- Anthology ID:
- 2022.smm4h-1.11
- Volume:
- Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Graciela Gonzalez-Hernandez, Davy Weissenbacher
- Venue:
- SMM4H
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 38–41
- Language:
- URL:
- https://aclanthology.org/2022.smm4h-1.11
- DOI:
- Cite (ACL):
- Pan He, Chen YuZe, and Yanru Zhang. 2022. Zhegu@SMM4H-2022: The Pre-training Tweet & Claim Matching Makes Your Prediction Better. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 38–41, Gyeongju, Republic of Korea. Association for Computational Linguistics.
- Cite (Informal):
- Zhegu@SMM4H-2022: The Pre-training Tweet & Claim Matching Makes Your Prediction Better (He et al., SMM4H 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2022.smm4h-1.11.pdf