利用目标分析视角的跨目标立场检测
Cross-target Stance Detection by Exploiting Target Analytical Perspectives
AI总结：
本文提出多视角提示微调模型MPPT，通过两阶段思维链方法TsCoT利用大语言模型生成目标分析视角及自然语言解释，并经框架MultiPLN融合至立场预测器，有效解决跨目标立场检测中的知识迁移问题。
摘要：
跨目标立场检测（CTSD）是一项重要任务，它通过利用源自源目标的标注数据来推断目标对象的立场。CTSD 中的一种重要方法是提取领域不变特征，以弥合多个目标之间的知识鸿沟。然而，非正式且短小的文本结构以及隐式表达，使得领域不变知识的提取变得复杂。本文提出了一种用于 CTSD 的多视角提示微调（MPPT）模型，该模型以分析视角为桥梁进行知识迁移。首先，我们开发了一种两阶段基于指令的思维链方法（TsCoT），通过基于大语言模型（LLM）构建指令，引导出目标分析视角，并从多个视角提供自然语言解释（NLEs）。其次，我们提出多视角提示微调框架（MultiPLN），将 NLEs 融合到立场预测器中。大量实验结果证明了 MPPT 相对于最先进基线方法的优越性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
