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 imperative 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 10,641 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.- Anthology ID:
- 2022.emnlp-main.676
- Volume:
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 9950–9969
- Language:
- URL:
- https://aclanthology.org/2022.emnlp-main.676
- DOI:
- Cite (ACL):
- Xinliang Frederick Zhang, Nick Beauchamp, and Lu Wang. 2022. Generative Entity-to-Entity Stance Detection with Knowledge Graph Augmentation. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 9950–9969, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- Generative Entity-to-Entity Stance Detection with Knowledge Graph Augmentation (Zhang et al., EMNLP 2022)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2022.emnlp-main.676.pdf