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。

通过利用目标观点进行跨目标立场检测

Cross-target Stance Detection by Exploiting Target Analytical  Perspectives

Stance detection is the process of inferring a person's position or
standpoint on a specific issue to deduce prevailing perceptions toward topics
of general or controversial interest, such as health policies during the
COVID-19 pandemic. Existing models for stance detection are trained to perform
well for a single domain (e.g., COVID-19) and a specific target topic (e.g.,
masking protocols), but are generally ineffectual in other domains or targets
due to distributional shifts in the data. However, constructing
high-performing, domain-specific stance detection models requires an extensive
corpus of labeled data relevant to the targeted domain, yet such datasets are
not readily available. This poses a challenge as the process of annotating data
is costly and time-consuming. To address these challenges, we introduce a novel
stance detection model coined domain-adaptive Cross-target STANCE detection via
Contrastive learning and Counterfactual generation (STANCE-C3) that uses
counterfactual data augmentation to enhance domain-adaptive training by
enriching the target domain dataset during the training process and requiring
significantly less information from the new domain. We also propose a modified
self-supervised contrastive learning as a component of STANCE-C3 to prevent
overfitting for the existing domain and target and enable cross-target stance
detection. Through experiments on various datasets, we show that STANCE-C3
shows performance improvement over existing state-of-the-art methods.

通过对比学习和反事实生成引入了一种新颖的领域自适应跨目标立场检测模型 STANC-C3，利用反事实数据增强培训过程中的目标领域数据集，并且需要更少来自新领域的信息，通过在多个数据集上的实验，表明 STANC-C3 相对于现有的最先进的方法显示了性能的提升。