---
title: "Zero-Shot and Few-Shot Stance Detection on Varied — Wen 2023"
description: "Stance detection decides whether a text supports, opposes, or is neutral toward a target."
authors: ["Haoyang Wen", "Alexander Hauptmann"]
year: 2023
venue: "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)"
source: "https://aclanthology.org/2023.acl-short.127/"
doi: "10.18653/v1/2023.acl-short.127"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/papers/wen-2023-zero-shot-few-shot-stance/"
---
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# Zero-Shot and Few-Shot Stance Detection on Varied Topics via Conditional Generation

**Authors:** Haoyang Wen, Alexander Hauptmann · **Year:** 2023 · **Venue:** Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2023.acl-short.127/) · [PDF](https://aclanthology.org/2023.acl-short.127.pdf) · [DOI 10.18653/v1/2023.acl-short.127](https://doi.org/10.18653/v1/2023.acl-short.127) · [OpenAlex](https://openalex.org/W4385570875)

**Citations:** 17 citations (openalex-snapshot · 2026-10-14) · [9](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Stance detection decides whether a text supports, opposes, or is neutral toward a target. The VAST benchmark makes this harder because most test targets appear rarely or never in training. Earlier VAST systems treat the task as classification, learning label vectors from scratch and ignoring the meaning of the label words and target phrase.

Wen and Hauptmann instead recast it as conditional generation with BART. The encoder receives the input text plus a short template that names the target and leaves a blank for the stance. The decoder then rewrites the template with the stance word filled in. Three additions strengthen this setup:
- an auxiliary task that generates the target from the text and a given stance;
- unlikelihood training that penalizes templates filled with wrong stance labels;
- target-related Wikipedia snippets appended to the input, following He et al. (2022).

The model sets a new state of the art on VAST. This suggests that using label and target semantics through generation helps models generalize to unseen topics.

## Key points
- **Data:** VAST has 18,548 examples from the New York Times "Room for Debate" section, covering 5,630 distinct targets. The original split of Allaway and McKeown (2020) is used, together with the Wikipedia background text that He et al. (2022) gathered.
- **Setup:** The base model is BART-base, comparable in size to BERT-base baselines. The total loss combines stance generation, target prediction (weight 1) and unlikelihood (weight 0.5). Results are averaged over 5 random seeds.
- **Ablation (dev macro-F1):**
  - BERT Classification: 72.1
  - BART w/ Template: 75.3
  - \+ target prediction: 75.7
  - \+ unlikelihood: 75.9
  - \+ Wikipedia: 77.4
- **Test macro-F1 (zero-shot / few-shot / overall):**
  - This model: 76.4 / 78.0 / 77.3
  - WS-BERT (previous best): 75.3 / 73.6 / 74.5
  - The model also beats TGA-Net, BERT-GCN and CKE-Net, with p \< 0.001 on overall F1.
- **Limitations:** The method needs many diverse training targets, was trained only on news debate text, and runs slower at inference than a classifier.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/)

## Cited by

Cited by

9 papers of our library cite it (of the 140673 whose bibliographies we read); 2 of them have a page here, 7 not yet.

- [Can Large Language Models Address Open-Target Stance Detection?](https://md.research.elamur.ai/free/papers/akash-2025-can-large-language-models-address/)
- [Acquired TASTE: Multimodal Stance Detection with Textual and Structural Embeddings](https://md.research.elamur.ai/free/papers/barel-2025-acquired-taste-multimodal-stance-detection/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{wen2023zero,
  title = {{Zero-Shot and Few-Shot Stance Detection on Varied Topics via Conditional Generation}},
  author = {Wen, Haoyang and Hauptmann, Alexander},
  booktitle = {Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  year = {2023},
  doi = {10.18653/v1/2023.acl-short.127},
  url = {https://doi.org/10.18653/v1/2023.acl-short.127}
}
```

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