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        <title>Interpretable Multimodal Misinformation Detection with Logic Reasoning</title>
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        <namePart type="given">Hui</namePart>
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    <abstract>Multimodal misinformation on online social platforms is becoming a critical concern due to increasing credibility and easier dissemination brought by multimedia content, compared to traditional text-only information. While existing multimodal detection approaches have achieved high performance, the lack of interpretability hinders these systems’ reliability and practical deployment. Inspired by Neural-Symbolic AI which combines the learning ability of neural networks with the explainability of symbolic learning, we propose a novel logic-based neural model for multimodal misinformation detection which integrates interpretable logic clauses to express the reasoning process of the target task. To make learning effective, we parameterize the symbolic logical elements using neural representations, which facilitate the automatic generation and evaluation of meaningful logic clauses. Additionally, to make our framework generalizable across diverse misinformation sources, we introduce five meta-predicates that can be instantiated with different correlations. Results on three public datasets (Twitter, Weibo, and Sarcasm) demonstrate the feasibility and versatility of our model.</abstract>
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%0 Conference Proceedings
%T Interpretable Multimodal Misinformation Detection with Logic Reasoning
%A Liu, Hui
%A Wang, Wenya
%A Li, Haoliang
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F liu-etal-2023-interpretable
%X Multimodal misinformation on online social platforms is becoming a critical concern due to increasing credibility and easier dissemination brought by multimedia content, compared to traditional text-only information. While existing multimodal detection approaches have achieved high performance, the lack of interpretability hinders these systems’ reliability and practical deployment. Inspired by Neural-Symbolic AI which combines the learning ability of neural networks with the explainability of symbolic learning, we propose a novel logic-based neural model for multimodal misinformation detection which integrates interpretable logic clauses to express the reasoning process of the target task. To make learning effective, we parameterize the symbolic logical elements using neural representations, which facilitate the automatic generation and evaluation of meaningful logic clauses. Additionally, to make our framework generalizable across diverse misinformation sources, we introduce five meta-predicates that can be instantiated with different correlations. Results on three public datasets (Twitter, Weibo, and Sarcasm) demonstrate the feasibility and versatility of our model.
%R 10.18653/v1/2023.findings-acl.620
%U https://aclanthology.org/2023.findings-acl.620
%U https://doi.org/10.18653/v1/2023.findings-acl.620
%P 9781-9796

Markdown (Informal)

[Interpretable Multimodal Misinformation Detection with Logic Reasoning](https://aclanthology.org/2023.findings-acl.620) (Liu et al., Findings 2023)

• Interpretable Multimodal Misinformation Detection with Logic Reasoning (Liu et al., Findings 2023)

ACL

• Hui Liu, Wenya Wang, and Haoliang Li. 2023. Interpretable Multimodal Misinformation Detection with Logic Reasoning. In Findings of the Association for Computational Linguistics: ACL 2023, pages 9781–9796, Toronto, Canada. Association for Computational Linguistics.