Abstract
The age of social media is rife with memes. Understanding and detecting harmful memes pose a significant challenge due to their implicit meaning that is not explicitly conveyed through the surface text and image. However, existing harmful meme detection approaches only recognize superficial harm-indicative signals in an end-to-end classification manner but ignore in-depth cognition of the meme text and image. In this paper, we attempt to detect harmful memes based on advanced reasoning over the interplay of multimodal information in memes. Inspired by the success of Large Language Models (LLMs) on complex reasoning, we first conduct abductive reasoning with LLMs. Then we propose a novel generative framework to learn reasonable thoughts from LLMs for better multimodal fusion and lightweight fine-tuning, which consists of two training stages: 1) Distill multimodal reasoning knowledge from LLMs; and 2) Fine-tune the generative framework to infer harmfulness. Extensive experiments conducted on three meme datasets demonstrate that our proposed approach achieves superior performance than state-of-the-art methods on the harmful meme detection task.
Original language | English |
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Title of host publication | Findings of the Association for Computational Linguistics |
Subtitle of host publication | EMNLP 2023 |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 9114-9128 |
Number of pages | 15 |
ISBN (Electronic) | 9798891760615 |
DOIs | |
Publication status | Published - Dec 2023 |
Event | 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023 - Resorts World Convention Centre , Singapore, Singapore Duration: 6 Dec 2023 → 10 Dec 2023 https://2023.emnlp.org/ (Conference website) |
Conference
Conference | 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023 |
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Country/Territory | Singapore |
City | Singapore |
Period | 6/12/23 → 10/12/23 |
Internet address |
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Scopus Subject Areas
- Computational Theory and Mathematics
- Computer Science Applications
- Information Systems
- Language and Linguistics
- Linguistics and Language