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
Bibkey:
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)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-1/2020.wnut-1.62.pdf
Code
 VinAIResearch/COVID19Tweet
Data
WNUT-2020 Task 2