InfoMiner at WNUT-2020 Task 2: Transformer-based Covid-19 Informative Tweet Extraction

Hansi Hettiarachchi, Tharindu Ranasinghe


Abstract
Identifying informative tweets is an important step when building information extraction systems based on social media. WNUT-2020 Task 2 was organised to recognise informative tweets from noise tweets. In this paper, we present our approach to tackle the task objective using transformers. Overall, our approach achieves 10th place in the final rankings scoring 0.9004 F1 score for the test set.
Anthology ID:
2020.wnut-1.49
Volume:
Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)
Month:
November
Year:
2020
Address:
Online
Venues:
EMNLP | WNUT
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
359–365
Language:
URL:
https://aclanthology.org/2020.wnut-1.49
DOI:
10.18653/v1/2020.wnut-1.49
Bibkey:
Cite (ACL):
Hansi Hettiarachchi and Tharindu Ranasinghe. 2020. InfoMiner at WNUT-2020 Task 2: Transformer-based Covid-19 Informative Tweet Extraction. In Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020), pages 359–365, Online. Association for Computational Linguistics.
Cite (Informal):
InfoMiner at WNUT-2020 Task 2: Transformer-based Covid-19 Informative Tweet Extraction (Hettiarachchi & Ranasinghe, WNUT 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.wnut-1.49.pdf
Code
 hhansi/informative-tweet-identification
Data
WNUT-2020 Task 2