mlsanityData to 14 Oct 2026

Unseen Target Stance Detection with Adversarial Domain Generalization

arXiv 2010.05471 ↗First posted 12 Oct 2020cs.CL cs.AIcited by 11

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Abstract (arXiv)

Although stance detection has made great progress in the past few years, it is still facing the problem of unseen targets. In this study, we investigate the domain difference between targets and thus incorporate attention-based conditional encoding with adversarial domain generalization to perform unseen target stance detection. Experimental results show that our approach achieves new state-of-the-art performance on the SemEval-2016 dataset, demonstrating the importance of domain difference between targets in unseen target stance detection.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Oct 2020.

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Citing papers per year: 2021: 5; 2022: 1; 2023: 4; 2024: 1.

11 papers.

  1. 2021Generalizing to Unseen Domains: A Survey on Domain Generalizationcited by 766
  2. 2021Towards Out-Of-Distribution Generalization: A Surveycited by 436
  3. 2021Towards Unsupervised Domain Generalizationcited by 40
  4. 2022Single-source Domain Expansion Network for Cross-Scene Hyperspectral Image Classificationcited by 25
  5. 2021Understanding and Testing Generalization of Deep Networks on Out-of-Distribution Datacited by 5
  6. 2023Domain-aware Triplet loss in Domain Generalizationcited by 5
  7. 2023Robust Stance Detection: Understanding Public Perceptions in Social Mediacited by 2
  8. 2023Domain Aligned Prefix Averaging for Domain Generalization in Abstractive Summarizationcited by 1
  9. 2023Towards Generalising Neural Topical Representationscited by 1
  10. 2024Zero-shot Cross-lingual Stance Detection via Adversarial Language Adaptationcited by 1
  11. 2021Human-in-the-Loop Disinformation Detection: Stance, Sentiment, or Something Else?cited by 0