Target-Aware Data Augmentation for Stance Detection (2021)

Abstract: The goal of stance detection is to identify whether the author of a text is in favor of, neutral or against a specific target. Despite substantial progress on this task, one of the remaining challenges is the scarcity of annotations. Data augmentation is commonly used to address annotation scarcity by generating more training samples. However, the augmented sentences that are generated by existing methods are either less diversified or inconsistent with the given target and stance label. In this paper, we formulate the data augmentation of stance detection as a conditional masked language modeling task and augment the dataset by predicting the masked word conditioned on both its context and the auxiliary sentence that contains target and label information. Moreover, we propose another simple yet effective method that generates target-aware sentence by replacing a target mention with the other. Experimental results show that our proposed methods significantly outperforms previous augmentation methods on 11 targets.

## introduction nowadays, people often take to social media to express their stances toward specific targets (e.g., political figures or abortion). these stances in an aggregate can provide valuable information for obtaining insight into some important events such as presidential elections. the goal of the stance detection task is to determine from a piece of text whether the author of the text is in favor of, neutral or against toward a specific target @xcite , which indicates that all elements, the sentence, the target, and the label, are used to train a stance detection model. we can further classify the task as single-target stance detection and multi-target stance detection @xcite where we need to detect the stances toward two different targets simultaneously. ## experiments in this section, we first describe three stance detection datasets used for evaluation and several baseline methods of data augmentation and stance detection. then, we introduce the evaluation metrics and report the experimental results. ## case study in this section, we present several augmented examples in ## conclusion in this paper, we presented two data augmentation methods, called asda and tr, for stance detection. different from the existing augmentation methods that are either unaware of target information or hard to be applied to different stance detection tasks, asda performs better in generating target-relevant and label-compatible sentences and can be easily applied to various tasks. results