Cross-target Stance Detection by Exploiting Target Analytical Perspectives

• URL: http://arxiv.org/abs/2401.01761v2
• Date: Thu, 4 Jan 2024 01:30:36 GMT
• Title: Cross-target Stance Detection by Exploiting Target Analytical Perspectives
• Authors: Daijun Ding, Rong Chen, Liwen Jing, Bowen Zhang, Xu Huang, Li Dong, Xiaowen Zhao, Ge Song
• Abstract summary: Cross-target stance detection (CTSD) is an important task, which infers the attitude of the destination target by utilizing annotated data from the source target. One important approach in CTSD is to extract domain-invariant features to bridge the knowledge gap between multiple targets. We propose a Multi-Perspective Prompt-Tuning (MPPT) model for CTSD that uses the analysis perspective as a bridge to transfer knowledge.
• Score: 22.320628580895164
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cross-target stance detection (CTSD) is an important task, which infers the attitude of the destination target by utilizing annotated data derived from the source target. One important approach in CTSD is to extract domain-invariant features to bridge the knowledge gap between multiple targets. However, the analysis of informal and short text structure, and implicit expressions, complicate the extraction of domain-invariant knowledge. In this paper, we propose a Multi-Perspective Prompt-Tuning (MPPT) model for CTSD that uses the analysis perspective as a bridge to transfer knowledge. First, we develop a two-stage instruct-based chain-of-thought method (TsCoT) to elicit target analysis perspectives and provide natural language explanations (NLEs) from multiple viewpoints by formulating instructions based on large language model (LLM). Second, we propose a multi-perspective prompt-tuning framework (MultiPLN) to fuse the NLEs into the stance predictor. Extensive experiments results demonstrate the superiority of MPPT against the state-of-the-art baseline methods.

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