Generative Entity-to-Entity Stance Detection with Knowledge Graph Augmentation

TL;DR

This paper introduces a new entity-to-entity stance detection task, supported by a novel generative model and a curated dataset, advancing understanding of media stance and entity ideology.

Contribution

The paper proposes the E2E stance detection task, creates a new dataset, and develops a generative model with knowledge graph augmentation for improved stance inference.

Findings

Model outperforms baselines significantly.

E2E stance detection aids media analysis.

Knowledge graph enhances model performance.

Abstract

Stance detection is typically framed as predicting the sentiment in a given text towards a target entity. However, this setup overlooks the importance of the source entity, i.e., who is expressing the opinion. In this paper, we emphasize the need for studying interactions among entities when inferring stances. We first introduce a new task, entity-to-entity (E2E) stance detection, which primes models to identify entities in their canonical names and discern stances jointly. To support this study, we curate a new dataset with 10,619 annotations labeled at the sentence-level from news articles of different ideological leanings. We present a novel generative framework to allow the generation of canonical names for entities as well as stances among them. We further enhance the model with a graph encoder to summarize entity activities and external knowledge surrounding the entities. Experiments show that our model outperforms strong comparisons by large margins. Further analyses demonstrate the usefulness of E2E stance detection for understanding media quotation and stance landscape, as well as inferring entity ideology.