mlsanityData to 7 Oct 2026

IITD at the WANLP 2022 Shared Task: Multilingual Multi-Granularity Network for Propaganda Detection

arXiv 2210.17190 ↗First posted 31 Oct 2022cs.CL cs.LGcited by 3

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Abstract (arXiv)

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 modeled 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 empirical 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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 8 of its 10 references that are in the map. First posted on arXiv 31 Oct 2022.

Builds on

8 of 10 references.

References · 8

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
  3. 2020AraBERT: Transformer-based Model for Arabic Language Understandingcited by 359
  4. 2021Word Alignment by Fine-tuning Embeddings on Parallel Corporacited by 186
  5. 2019Fine-Grained Analysis of Propaganda in News Articlescited by 130
  6. 2021SemEval-2021 Task 6: Detection of Persuasion Techniques in Texts and Imagescited by 49
  7. 2021Detecting Propaganda Techniques in Memescited by 43
  8. 2019Proppy: A System to Unmask Propaganda in Online Newscited by 16

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Citing papers per year: 2022: 1; 2023: 1; 2024: 1.

3 papers.