# Unseen Target Stance Detection with Adversarial Domain Generalization

arXiv: https://arxiv.org/abs/2010.05471 · HTML: https://mlsanity.com/paper/2010.05471/

## Key facts

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

## Built on by (most cited first, 11 of 11)

- [Generalizing to Unseen Domains: A Survey on Domain Generalization](https://mlsanity.com/paper/2103.03097/) (2021)
- [Towards Out-Of-Distribution Generalization: A Survey](https://mlsanity.com/paper/2108.13624/) (2021)
- [Towards Unsupervised Domain Generalization](https://mlsanity.com/paper/2107.06219/) (2021)
- [Single-source Domain Expansion Network for Cross-Scene Hyperspectral Image Classification](https://mlsanity.com/paper/2209.01634/) (2022)
- [Understanding and Testing Generalization of Deep Networks on Out-of-Distribution Data](https://mlsanity.com/paper/2111.09190/) (2021)
- [Domain-aware Triplet loss in Domain Generalization](https://mlsanity.com/paper/2303.01233/) (2023)
- [Robust Stance Detection: Understanding Public Perceptions in Social Media](https://mlsanity.com/paper/2309.15176/) (2023)
- [Domain Aligned Prefix Averaging for Domain Generalization in Abstractive Summarization](https://mlsanity.com/paper/2305.16820/) (2023)
- [Towards Generalising Neural Topical Representations](https://mlsanity.com/paper/2307.12564/) (2023)
- [Zero-shot Cross-lingual Stance Detection via Adversarial Language Adaptation](https://mlsanity.com/paper/2404.14339/) (2024)
- [Human-in-the-Loop Disinformation Detection: Stance, Sentiment, or Something Else?](https://mlsanity.com/paper/2111.05139/) (2021)

## 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.