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20232026
most citedA Logically Consistent Chain-of-Thought Approach for Stance Detection

5 citations · 12 across the 15 of their papers we have counts for

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Showing 2024Show all

6 papers · 1 filter

cs.CL2024

DualCoTs: Dual Chain-of-Thoughts Prompting for Sentiment Lexicon Expansion of Idioms

Fuqiang Niu, Minghuan Tan, Bowen Zhang +2

Idioms represent a ubiquitous vehicle for conveying sentiments in the realm of everyday discourse, rendering the nuanced analysis of idiom sentiment crucial for a comprehensive und…

cs.CL2024★ 2 cited

A Survey of Stance Detection on Social Media: New Directions and Perspectives

Bowen Zhang, Genan Dai, Fuqiang Niu +5

In modern digital environments, users frequently express opinions on contentious topics, providing a wealth of information on prevailing attitudes. The systematic analysis of these…

cs.MM2024

Multimodal Multi-turn Conversation Stance Detection: A Challenge Dataset and Effective Model

Fuqiang Niu, Zebang Cheng, Xianghua Fu +5

Stance detection, which aims to identify public opinion towards specific targets using social media data, is an important yet challenging task. With the proliferation of diverse mu…

cs.CL2024

MIPS at SemEval-2024 Task 3: Multimodal Emotion-Cause Pair Extraction in Conversations with Multimodal Language Models

Zebang Cheng, Fuqiang Niu, Yuxiang Lin +3

This paper presents our winning submission to Subtask 2 of SemEval 2024 Task 3 on multimodal emotion cause analysis in conversations. We propose a novel Multimodal Emotion Recognit…

cs.CL2024★ 2 cited

A Challenge Dataset and Effective Models for Conversational Stance Detection

Fuqiang Niu, Min Yang, Ang Li +3

Previous stance detection studies typically concentrate on evaluating stances within individual instances, thereby exhibiting limitations in effectively modeling multi-party discus…

cs.CL2024★ 3 cited

MoZIP: A Multilingual Benchmark to Evaluate Large Language Models in Intellectual Property

Shiwen Ni, Minghuan Tan, Yuelin Bai +9

Large language models (LLMs) have demonstrated impressive performance in various natural language processing (NLP) tasks. However, there is limited understanding of how well LLMs p…