MemeMQA: Multimodal Question Answering for Memes via Rationale-Based Inferencing
<|reference_start|>MemeMQA: Multimodal Question Answering for Memes via Rationale-Based Inferencing: Memes have evolved as a prevalent medium for diverse communication, ranging from humour to propaganda. With the rising popularity of image-focused content, there is a growing need to explore its potential harm from different aspects. Previous studies have analyzed memes in closed settings - detecting harm, applying semantic labels, and offering natural language explanations. To extend this research, we introduce MemeMQA, a multimodal question-answering framework aiming to solicit accurate responses to structured questions while providing coherent explanations. We curate MemeMQACorpus, a new dataset featuring 1,880 questions related to 1,122 memes with corresponding answer-explanation pairs. We further propose ARSENAL, a novel two-stage multimodal framework that leverages the reasoning capabilities of LLMs to address MemeMQA. We benchmark MemeMQA using competitive baselines and demonstrate its superiority - ~18% enhanced answer prediction accuracy and distinct text generation lead across various metrics measuring lexical and semantic alignment over the best baseline. We analyze ARSENAL's robustness through diversification of question-set, confounder-based evaluation regarding MemeMQA's generalizability, and modality-specific assessment, enhancing our understanding of meme interpretation in the multimodal communication landscape.<|reference_end|>
@article{agarwal2024mememqa:,
    title={MemeMQA: Multimodal Question Answering for Memes via Rationale-Based
  Inferencing},
    author={Siddhant Agarwal, Shivam Sharma, Preslav Nakov, Tanmoy Chakraborty},
    journal={arXiv preprint arXiv:2405.11215},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.11215},
    primaryClass={cs.CL cs.CY}
}