Few-Shot Stance Detection via Target-Aware Prompt Distillation

Abstract

Stance detection aims to identify whether the author of a text is in favor of, against, or neutral to a given target. The main challenge of this task comes two-fold: few-shot learning resulting from the varying targets and the lack of contextual information of the targets. Existing works mainly focus on solving the second issue by designing attention-based models or introducing noisy external knowledge, while the first issue remains under-explored. In this paper, inspired by the potential capability of pre-trained language models (PLMs) serving as knowledge bases and few-shot learners, we propose to introduce prompt-based fine-tuning for stance detection. PLMs can provide essential contextual information for the targets and enable few-shot learning via prompts. Considering the crucial role of the target in stance detection task, we design targetaware prompts and propose a novel verbalizer. Instead of mapping each label to a concrete word, our verbalizer maps each label to a vector and picks the label that best captures the correlation between the stance and the target. Moreover, to alleviate the possible defect of dealing with varying targets with a single hand-crafted prompt, we propose to distill the information learned from multiple prompts. Experimental results show the superior performance of our proposed model in both full-data and few-shot scenarios.

Leveraging Chain-of-Thought to Enhance Stance Detection with Prompt-Tuning

“…Automatic verbalizers are designed using search methods, but they require a significant number of training and validation sets to optimize [35]. Previous studies on prompt-based models have concentrated on stance detection [15,36]. Jiang et al [36] presented TAPD, a prompt-tuning framework designed for stance detection.…”

“…Previous studies on prompt-based models have concentrated on stance detection [15,36]. Jiang et al [36] presented TAPD, a prompt-tuning framework designed for stance detection. TAPD utilizes a verbalizer that maps labels to hidden vectors to facilitate label prediction.…”

“…Performance comparison on F1 avg . The results with † are retrieved from [13]; ‡ are retrieved from [36]. The ¶ mark refers to a p-value < 0.05.…”

Labels Need Prompts Too: Mask Matching for Natural Language Understanding Tasks

“…Large-scale pre-trained language models (PLMs) such as BERT (Devlin et al 2019) and RoBERTa (Liu et al 2019) have achieved impressive performances on a wide range of natural language understanding (NLU) tasks, e.g., topic classification (Xu, Liu, and Abbasnejad 2022;Wu et al 2022), sentiment analysis (Zhang et al 2022), information extraction (Li et al 2020;Lu et al 2022), natural language inference (Dawkins 2021;Nighojkar and Licato 2021), and stance detection (Liu et al 2021c;Jiang et al 2022b).…”

Distantly Supervised Explainable Stance Detection via Chain-of-Thought Supervision

“…• PIN-POM [47] puts forth a soft prompt approach tailored for short text categorization, an adaptation readily amenable to stance detection tasks. • TAPD [48] uses a prompt setting method for position detection, using PLM to learn effective representations for stance detection tasks.…”