mlsanityData to 7 Oct 2026

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

arXiv 2211.01467 ↗First posted 2 Nov 2022cs.CLcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 33 of its 74 references that are in the map. First posted on arXiv 2 Nov 2022.

Builds on

33 of 74 references.

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Cited by year

Citing papers per year: 2023: 4; 2024: 2.

6 papers.

  1. 2024ULTRA: Unleash LLMs' Potential for Event Argument Extraction through Hierarchical Modeling and Pair-wise Self-Refinementcited by 12
  2. 2023MOKA: Moral Knowledge Augmentation for Moral Event Extractioncited by 7
  3. 2023"We Demand Justice!": Towards Social Context Grounding of Political Textscited by 4
  4. 2023All Things Considered: Detecting Partisan Events from News Media with Cross-Article Comparisoncited by 2
  5. 2024TOBUGraph: Knowledge Graph-Based Retrieval for Enhanced LLM Performance Beyond RAGcited by 2
  6. 2023Crossing the Aisle: Unveiling Partisan and Counter-Partisan Events in News Reportingcited by 1