Adversarial Network with External Knowledge for Zero-Shot Stance Detection (2023)

Abstract: “Zero-shot stance detection intends to detect previously unseen targets’ stances in the testingphase. However, achieving this goal can be difficult, as it requires minimizing the domain trans-fer between different targets, and improving the model’s inference and generalization abilities. To address this challenge, we propose an adversarial network with external knowledge (ANEK)model. Specifically, we adopt adversarial learning based on pre-trained models to learn transfer-able knowledge from the source targets, thereby enabling the model to generalize well to unseentargets. Additionally, we incorporate sentiment information and common sense knowledge intothe contextual representation to further enhance the model’s understanding. Experimental re-sults on several datasets reveal that our method achieves excellent performance, demonstratingits validity and feasibility.”

## introduction stance detection @xcite @xcite is a significant task in nlp, focusing on identifying the stance (e.g., against, favor, or neutral) conveyed in the text towards a given target. it can be efficiently applied to social opinion analysis @xcite , rumor detection @xcite , and other research fields by mining text opinions. traditional intra-target stance detection @xcite has limited applications since it requires training and testing under the same target and depends heavily on labeled data to achieve excellent performance. with the frequent and vast updates of topics on social platforms, manually labeling new targets becomes expensive and time-consuming, making it impractical to create a labeled dataset with all potential targets @xcite . therefore, the study of zero-shot stance detection @xcite for unseen targets is essential and promising. to tackle the zero-shot stance detection task, existing works generally incorporate external knowledge @xcite as support for inference or introduce attention mechanisms @xcite to capture the relationships between targets, which do not explicitly model of the transferable knowledge between source and destination targets. some methods solely focus on employing adversarial training @xcite to learn a target-invariant representation of the text content, disregarding the possibility that the model may encounter challenges in correctly predicting sentences that contain implicit viewpoints or require more profound understanding. for example 1 in ## method the structure of our anek model is displayed in figure 1 , which mainly contains two parts. (1) knowledge graph training: we train a graph autoencoder using conceptnet relation subgraphs. ## conclusion this paper proposes an adversarial network with external knowledge (anek) to handle the zero-shot stance detection task. the model applies adversarial learning based on pre-trained models to ensure knowledge transferability, and introduces common sense knowledge and sentiment information to enhance the model's deep understanding and assist stance detection. in addition, stance contrastive learning is used to improve the model's generalization. the experimental results on three benchmark datasets indicate that our method performs competitively on some unseen targets. in future work, we will design a data enhancement method to alleviate the data scarcity problem in zero-shot settings and improve performance.