Abstract
In this paper, we describe our system for the shared task on Fighting the COVID-19 Infodemic in the English Language. Our proposed architecture consists of a multi-output classification model for the seven tasks, with a task-wise multi-head attention layer for inter-task information aggregation. This was built on top of the Bidirectional Encoder Representations obtained from the RoBERTa Transformer. We were able to achieve a mean F1 score of 0.891 on the test data, leading us to the second position on the test-set leaderboard.- Anthology ID:
- 2021.nlp4if-1.16
- Volume:
- Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda
- Month:
- June
- Year:
- 2021
- Address:
- Online
- Editors:
- Anna Feldman, Giovanni Da San Martino, Chris Leberknight, Preslav Nakov
- Venue:
- NLP4IF
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 110–114
- Language:
- URL:
- https://aclanthology.org/2021.nlp4if-1.16
- DOI:
- 10.18653/v1/2021.nlp4if-1.16
- Cite (ACL):
- Ayush Suhane and Shreyas Kowshik. 2021. Multi Output Learning using Task Wise Attention for Predicting Binary Properties of Tweets : Shared-Task-On-Fighting the COVID-19 Infodemic. In Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda, pages 110–114, Online. Association for Computational Linguistics.
- Cite (Informal):
- Multi Output Learning using Task Wise Attention for Predicting Binary Properties of Tweets : Shared-Task-On-Fighting the COVID-19 Infodemic (Suhane & Kowshik, NLP4IF 2021)
- PDF:
- https://preview.aclanthology.org/cschoel_rss_and_blog/2021.nlp4if-1.16.pdf