InterCLIP-MEP: Interactive CLIP and Memory-Enhanced Predictor for Multi-modal Sarcasm Detection

• URL: http://arxiv.org/abs/2406.16464v5
• Date: Mon, 16 Dec 2024 04:13:38 GMT
• Title: InterCLIP-MEP: Interactive CLIP and Memory-Enhanced Predictor for Multi-modal Sarcasm Detection
• Authors: Junjie Chen, Hang Yu, Subin Huang, Sanmin Liu, Linfeng Zhang,
• Abstract summary: Sarcasm in social media, often expressed through text-image combinations, poses challenges for sentiment analysis and intention mining. We propose InterCLIP-MEP, which introduces Interactive CLIP with an efficient training strategy to extract enriched text-image representations. We show that InterCLIP-MEP achieves state-of-the-art performance, with significant accuracy and F1 score improvements on MMSD and MMSD2.0.
• Score: 17.55808303452098
• License:
• Abstract: Sarcasm in social media, often expressed through text-image combinations, poses challenges for sentiment analysis and intention mining. Current multi-modal sarcasm detection methods have been demonstrated to overly rely on spurious cues within the textual modality, revealing a limited ability to genuinely identify sarcasm through nuanced text-image interactions. To solve this problem, we propose InterCLIP-MEP, which introduces Interactive CLIP (InterCLIP) with an efficient training strategy to extract enriched text-image representations by embedding cross-modal information directly into each encoder. Additionally, we design a Memory-Enhanced Predictor (MEP) with a dynamic dual-channel memory that stores valuable test sample knowledge during inference, acting as a non-parametric classifier for robust sarcasm recognition. Experiments on two benchmarks demonstrate that InterCLIP-MEP achieves state-of-the-art performance, with significant accuracy and F1 score improvements on MMSD and MMSD2.0. Our code is available at https://github.com/CoderChen01/InterCLIP-MEP.

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