Abstract
We present our system for the two subtasks of the shared task on propaganda detection in Arabic, part of WANLP’2022. Subtask 1 is a multi-label classification problem to find the propaganda techniques used in a given tweet. Our system for this task uses XLM-R to predict probabilities for the target tweet to use each of the techniques. In addition to finding the techniques, subtask 2 further asks to identify the textual span for each instance of each technique that is present in the tweet; the task can be modelled as a sequence tagging problem. We use a multi-granularity network with mBERT encoder for subtask 2. Overall, our system ranks second for both subtasks (out of 14 and 3 participants, respectively). Our experimental results and analysis show that it does not help to use a much larger English corpus annotated with propaganda techniques, regardless of whether used in English or after translation to Arabic.- Anthology ID:
- 2022.wanlp-1.63
- 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:
- 529–533
- Language:
- URL:
- https://aclanthology.org/2022.wanlp-1.63
- DOI:
- 10.18653/v1/2022.wanlp-1.63
- Cite (ACL):
- Shubham Mittal and Preslav Nakov. 2022. IITD at WANLP 2022 Shared Task: Multilingual Multi-Granularity Network for Propaganda Detection. In Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP), pages 529–533, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- IITD at WANLP 2022 Shared Task: Multilingual Multi-Granularity Network for Propaganda Detection (Mittal & Nakov, WANLP 2022)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2022.wanlp-1.63.pdf