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Fenghuan Li

4 accepted papers

2025

Ambiguity-aware Multi-level Incongruity Fusion Network for Multi-Modal Sarcasm Detection

COLING 2025main

Multi-modal sarcasm detection aims to identify whether a given image-text pair is sarcastic. The pivotal factor of the task lies in accurately capturing incongruities from different modalities. Although existing studies have achieved impressive success, they primarily committed to fusing the textual…

Cited by 0SourcePDFScholar
2025

SACR: Self-training with Saliency-Augmented Consistency Regularization for Few-Shot Learners

ICASSP 2025accepted

Pre-trained language models have made significant strides in natural language processing tasks, enabling flexible fine-tuning for downstream applications. However, in few-shot learning scenarios, pre-trained models face challenges related to overfitting due to limited training samples, which hinders…

Cited by 0SourceScholar
2024

Prompt-enhanced Network for Hateful Meme Classification

IJCAI 2024poster

The dynamic expansion of social media has led to an inundation of hateful memes on media platforms, accentuating the growing need for efficient identification and removal. Acknowledging the constraints of conventional multimodal hateful meme classification, which heavily depends on external knowledg…

2024

Semantics-Aware Dual Graph Convolutional Networks for Argument Pair Extraction

COLING 2024main

Argument pair extraction (APE) is a task that aims to extract interactive argument pairs from two argument passages. Generally, existing works focus on either simple argument interaction or task form conversion, instead of thorough deep-level feature exploitation of argument pairs. To address this i…

Cited by 0SourcePDFScholar