Abstract
The spread of propaganda through the internet has increased drastically over the past years. Lately, propaganda detection has started gaining importance because of the negative impact it has on society. In this work, we describe our approach for the WANLP 2022 shared task which handles the task of propaganda detection in a multi-label setting. The task demands the model to label the given text as having one or more types of propaganda techniques. There are a total of 21 propaganda techniques to be detected. We show that an ensemble of five models performs the best on the task, scoring a micro-F1 score of 59.73%. We also conduct comprehensive ablations and propose various future directions for this work.- Anthology ID:
- 2022.wanlp-1.60
- Volume:
- Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP)
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates (Hybrid)
- Editors:
- Houda Bouamor, Hend Al-Khalifa, Kareem Darwish, Owen Rambow, Fethi Bougares, Ahmed Abdelali, Nadi Tomeh, Salam Khalifa, Wajdi Zaghouani
- Venue:
- WANLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 515–519
- Language:
- URL:
- https://aclanthology.org/2022.wanlp-1.60
- DOI:
- 10.18653/v1/2022.wanlp-1.60
- Cite (ACL):
- Tanmay Chavan and Aditya Manish Kane. 2022. ChavanKane at WANLP 2022 Shared Task: Large Language Models for Multi-label Propaganda Detection. In Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP), pages 515–519, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- ChavanKane at WANLP 2022 Shared Task: Large Language Models for Multi-label Propaganda Detection (Chavan & Kane, WANLP 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2022.wanlp-1.60.pdf