IDSOU at WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets
Sora Ohashi, Tomoyuki Kajiwara, Chenhui Chu, Noriko Takemura, Yuta Nakashima, Hajime Nagahara
Abstract
We introduce the IDSOU submission for the WNUT-2020 task 2: identification of informative COVID-19 English Tweets. Our system is an ensemble of pre-trained language models such as BERT. We ranked 16th in the F1 score.- Anthology ID:
- 2020.wnut-1.62
- Volume:
- Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Wei Xu, Alan Ritter, Tim Baldwin, Afshin Rahimi
- Venue:
- WNUT
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 428–433
- Language:
- URL:
- https://aclanthology.org/2020.wnut-1.62
- DOI:
- 10.18653/v1/2020.wnut-1.62
- Cite (ACL):
- Sora Ohashi, Tomoyuki Kajiwara, Chenhui Chu, Noriko Takemura, Yuta Nakashima, and Hajime Nagahara. 2020. IDSOU at WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets. In Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020), pages 428–433, Online. Association for Computational Linguistics.
- Cite (Informal):
- IDSOU at WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets (Ohashi et al., WNUT 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2020.wnut-1.62.pdf
- Code
- VinAIResearch/COVID19Tweet
- Data
- WNUT-2020 Task 2