---
title: "CYUT at SemEval-2026 Task 3 — paper by Wu et al. (2026)"
description: "The paper presents CYUT's entry for SemEval-2026 Task 3 Track B. The task asks systems to predict… 21 references, cited by 1 paper here."
authors: ["Shih-Hung Wu", "Xian-Yan Chen", "Yi-Min Jian"]
year: 2026
venue: "Proceedings of the 20th International Workshop on Semantic Evaluation (2026)"
source: "https://aclanthology.org/2026.semeval-1.23/"
doi: "10.18653/v1/2026.semeval-1.23"
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/en/papers/wu-2026-cyut-semeval-2026-task-3/"
---
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# CYUT at SemEval-2026 Task 3: Multi-Task Dimensional Aspect Sentiment Regression with Polar Multi-Zone Labeling in VA Space

**Authors:** [Shih-Hung Wu](https://md.research.elamur.ai/free/en/authors/shih-hung-wu/), Xian-Yan Chen and [Yi-Min Jian](https://md.research.elamur.ai/free/en/authors/yi-min-jian/) · **Year:** 2026 · **Venue:** [Proceedings of the 20th International Workshop on Semantic Evaluation (2026)](https://md.research.elamur.ai/free/en/venues/semeval/) ([SemEval 2026](https://md.research.elamur.ai/free/en/venues/semeval/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.semeval-1.23/) · [PDF](https://aclanthology.org/2026.semeval-1.23.pdf) · [DOI 10.18653/v1/2026.semeval-1.23](https://doi.org/10.18653/v1/2026.semeval-1.23) · [OpenAlex](https://openalex.org/W7166883849)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · 1 in our library, of the 149,439 papers whose bibliographies we read

## Summary
The paper presents CYUT's entry for SemEval-2026 Task 3 Track B. The task asks systems to predict continuous Valence–Arousal (VA) scores, each in the range [1, 9], for a text and a target aspect across several languages. Systems are scored by RMSE. The authors split each text into one instance per aspect. The main task is VA regression, trained alongside auxiliary classification tasks generated automatically from the gold scores: polarity, intensity and VA quadrant.

Fixed-threshold auxiliary labels can leave some areas of the VA plane with very few examples. To address this, the authors add Polar Multi-Zone Labeling (PMZL-7). It treats the VA plane in polar coordinates around (5, 5). Samples closest to the centre form a neutral core, and the remaining samples are split by angle into six zones holding roughly equal numbers of examples.

They test XLM-R, Qwen2 and Ministral. PMZL-7 helps the two generative models but not XLM-R. The Qwen2 system beats both official baselines and achieves the top score among all teams on Nigerian Pidgin. The work suggests that auxiliary labels which account for the data distribution are worth comparing, although their benefit depends on the backbone.

## Key points
- The auxiliary labels are defined as follows:
  - **Polarity:** positive if V ≥ 5.5, negative if V ≤ 4.5, otherwise neutral.
  - **Intensity:** excited if A ≥ 6.0, calm if A ≤ 4.0, otherwise medium.
  - **Quadrant:** split at the point (5, 5).
  - **PMZL-7:** the 20% of samples nearest the centre become the neutral zone, and the rest fall into six equal-mass angular zones.
- The backbones are XLM-RoBERTa-base, Qwen2-7B and Ministral-7B. The generative models produce outputs in a "V#A" format, with regex recovery and clipping to [1, 9] when parsing fails.
- Official test average RMSE is 1.3999 for Qwen2-7B, 1.4817 for Ministral-7B and 1.6612 for XLM-R. The baselines score 2.4604 (mBERT) and 1.6024 (Mistral-3 14B). Qwen2 reaches 1.1024 on PCM (Nigerian Pidgin), the best result in the competition.
- Development-set ablations cover ENG, DEU, ZHO, PCM and SWA:
  - Adding PMZL-7 lowers average RMSE for Qwen2 (1.5462 → 1.5184) and Ministral (1.4372 → 1.4154).
  - For XLM-R, the model performs slightly better without PMZL-7 (1.5615 vs 1.5820).
  - Dropping intensity supervision hurts all three backbones.
- Code is released at github.com/YiMin0130/CYUT-SemEval2026-Task3. Planned future work includes other zone counts and soft zone assignment.

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

## Institutions

- [Chaoyang University of Technology](https://md.research.elamur.ai/free/en/universities/chaoyang-university-of-technology/) — University, Taichung, Taiwan · 2 works · 2 citations

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## Cite

Cite as: Shih-Hung Wu, Xian-Yan Chen and Yi-Min Jian. 2026. CYUT at SemEval-2026 Task 3: Multi-Task Dimensional Aspect Sentiment Regression with Polar Multi-Zone Labeling in VA Space. Proceedings of the 20th International Workshop on Semantic Evaluation (2026).

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

```bibtex
@inproceedings{wu2026cyut,
  title = {{CYUT at SemEval-2026 Task 3: Multi-Task Dimensional Aspect Sentiment Regression with Polar Multi-Zone Labeling in VA Space}},
  author = {Shih-Hung Wu, Xian-Yan Chen and Yi-Min Jian},
  booktitle = {Proceedings of the 20th International Workshop on Semantic Evaluation (2026)},
  year = {2026},
  doi = {10.18653/v1/2026.semeval-1.23},
  url = {https://doi.org/10.18653/v1/2026.semeval-1.23}
}
```

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*Updated 2 November 2026*