TEST_POSITIVE at W-NUT 2020 Shared Task-3: Cross-task modeling
Chacha Chen, Chieh-Yang Huang, Yaqi Hou, Yang Shi, Enyan Dai, Jiaqi Wang
Abstract
The competition of extracting COVID-19 events from Twitter is to develop systems that can automatically extract related events from tweets. The built system should identify different pre-defined slots for each event, in order to answer important questions (e.g., Who is tested positive? What is the age of the person? Where is he/she?). To tackle these challenges, we propose the Joint Event Multi-task Learning (JOELIN) model. Through a unified global learning framework, we make use of all the training data across different events to learn and fine-tune the language model. Moreover, we implement a type-aware post-processing procedure using named entity recognition (NER) to further filter the predictions. JOELIN outperforms the BERT baseline by 17.2% in micro F1.- Anthology ID:
- 2020.wnut-1.76
- Volume:
- Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Venue:
- WNUT
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 499–504
- Language:
- URL:
- https://aclanthology.org/2020.wnut-1.76
- DOI:
- 10.18653/v1/2020.wnut-1.76
- Cite (ACL):
- Chacha Chen, Chieh-Yang Huang, Yaqi Hou, Yang Shi, Enyan Dai, and Jiaqi Wang. 2020. TEST_POSITIVE at W-NUT 2020 Shared Task-3: Cross-task modeling. In Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020), pages 499–504, Online. Association for Computational Linguistics.
- Cite (Informal):
- TEST_POSITIVE at W-NUT 2020 Shared Task-3: Cross-task modeling (Chen et al., WNUT 2020)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2020.wnut-1.76.pdf