Abstract
The paper introduces the methodology proposed for the shared Task 2 of the Social Media Mining for Health Application (SMM4H) in 2022. Task 2 consists of two subtasks: Stance Detection and Premise Classification, named Subtask 2a and Subtask 2b, respectively. Our proposed system is based on dual-view attention neural networks and achieves an F1 score of 0.618 for Subtask 2a (0.068 more than the median) and an F1 score of 0.630 for Subtask 2b (0.017 less than the median). Further experiments show that the domain-specific pre-trained model, cross-validation, and pseudo-label techniques contribute to the improvement of system performance.- Anthology ID:
- 2022.smm4h-1.7
- 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
- Venue:
- SMM4H
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 23–26
- Language:
- URL:
- https://aclanthology.org/2022.smm4h-1.7
- DOI:
- Cite (ACL):
- Huabin Yang, Zhongjian Zhang, and Yanru Zhang. 2022. yiriyou@SMM4H’22: Stance and Premise Classification in Domain Specific Tweets with Dual-View Attention Neural Networks. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 23–26, Gyeongju, Republic of Korea. Association for Computational Linguistics.
- Cite (Informal):
- yiriyou@SMM4H’22: Stance and Premise Classification in Domain Specific Tweets with Dual-View Attention Neural Networks (Yang et al., SMM4H 2022)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2022.smm4h-1.7.pdf