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.

本文提出了一种多角度提示调整模型（MPPT）用于跨目标立场检测（CTSD），利用分析视角作为知识转移的桥梁，通过提供多视角的自然语言解释来融合到立场预测中，实验证明了 MPPT 在 CTSD 任务中的优越性。