Zero-Shot and Few-Shot Stance Detection on Varied Topics via Conditional Generation (2023)

Abstract: Zero-shot and few-shot stance detection identify the polarity of text with regard to a certain target when we have only limited or no training resources for the target. Previous work generally formulates the problem into a classification setting, ignoring the potential use of label text. In this paper, we instead utilize a conditional generation framework and formulate the problem as denoising from partially-filled templates, which can better utilize the semantics among input, label, and target texts. We further propose to jointly train an auxiliary task, target prediction, and to incorporate manually constructed incorrect samples with unlikelihood training to improve the representations for both target and label texts. We also verify the effectiveness of target-related Wikipedia knowledge with the generation framework. Experiments show that our proposed method significantly outperforms several strong baselines on VAST, and achieves new state-of-the-art performance.

## introduction stance detection is an important task that identifies the polarity of text with regard to certain target @xcite @xcite @xcite , as shown in previous work on stance detection mostly focuses on in-domain or leave-out targets with only a few target choices @xcite @xcite @xcite @xcite input text: airports and the roads on east nor west coast can not handle the present volume adequately as is. i did ride the vast trains in europe, japan and china and found them very comfortable and providing much better connections and more efficient. target: high-speed rail stance label: supportive (pro) however, connections among text semantics from input text, target, and label can be beneficial for stance detection. in this paper, we propose a new model by formulating the problem as a denoising task from text templates via conditional generation. compared to direct classification, we can further exploit the label and topic semantics via learning to decode a series of natural language text containing the predicted label. the denoising scheme can also take advantage of the pretrained language model with similar pretraining task formulation @xcite . to improve the target representation, we propose to jointly train target prediction with stance detection, which gives the input text and desired stance label to output possible targets. we use unlikelihood training @xcite ) that suppress the likelihood of manually constructed incorrect samples to enhance label bart @xmath0. stance @xmath1. </@xmath2... bart @xmath3. stance is supportive. </@xmath4: overall framework of bart-based generation framework for stance detection. representations. recently, he et al. (2022) show the effectiveness of target-related wikipedia knowledge for classification-based stance detection. we also follow the idea and incorporate target-related wikipedia knowledge for our generation model. we evaluate our method on vast. experimental results show that the conditional generation formulation can achieve better performance compared to classification, demonstrating the effectiveness of connecting input, target, and label semantics for stance detection. further analysis illustrates the benefits of joint target prediction, unlikelihood training, and wikipedia knowledge. our model can achieve new state-of-the-art performance, outperforming several strong baselines from previous work. ## approach in this section, we will discuss our approach to zero-shot and few-shot stance detection. we will first introduce the problem formulation, and then discuss our generation-based framework. ## conclusion in this paper, we propose a generation-based framework for zero-shot and few-shot stance detection that generate stance label from pre-defined templates. we further propose an auxiliary task, joint target prediction that takes stance label and input text to generate targets, and unlikelihood training on manually constructed incorrect generation output. combining with wikipedia knowledge for target from @xcite , our model can achieve new state-of-the-art performance on vast.