---
title: "Tewodros at SemEval-2025 Task 11: Multilingual Emotion"
description: "The paper describes Team Tewodros's entry to SemEval-2025 Task 11, Track B. The task asks systems to predict how… 92 references."
authors: ["Mikiyas Eyasu", "Wendmnew Sitot Abebaw", "Nida Hafeez", "Fatima Uroosa", "Tewodros Achamaleh Bizuneh", "Grigori Sidorov", "Alexander Gelbukh"]
year: 2025
venue: "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)"
source: "https://aclanthology.org/2025.semeval-1.196/"
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/eyasu-2025-tewodros-semeval-2025-task-11/"
---
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# Tewodros at SemEval-2025 Task 11: Multilingual Emotion Intensity Detection using Small Language Models

**Authors:** Mikiyas Eyasu, Wendmnew Sitot Abebaw, Nida Hafeez, [Fatima Uroosa](https://md.research.elamur.ai/free/en/authors/fatima-uroosa/), [Tewodros Achamaleh Bizuneh](https://md.research.elamur.ai/free/en/authors/tewodros-achamaleh-bizuneh/), [Grigori Sidorov](https://md.research.elamur.ai/free/en/authors/grigori-sidorov/) and [Alexander Gelbukh](https://md.research.elamur.ai/free/en/authors/alexander-gelbukh/) · **Year:** 2025 · **Venue:** [Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)](https://md.research.elamur.ai/free/en/venues/semeval/) ([SemEval 2025](https://md.research.elamur.ai/free/en/venues/semeval/2025/)) · **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/2025.semeval-1.196/) · [PDF](https://aclanthology.org/2025.semeval-1.196.pdf)

## Summary
The paper describes Team Tewodros's entry to SemEval-2025 Task 11, Track B. The task asks systems to predict how strongly each of six emotions (anger, disgust, fear, joy, sadness, surprise) is expressed in a text, on an ordinal 0–3 scale, for Amharic, English and Spanish. The organisers' multi-label data was used with their train/dev/test splits.

Instead of one shared multilingual system, the team fine-tuned a separate pretrained encoder for each language:
- afro-xlmr-large-76L for Amharic,
- DeBERTa-v3-base for English,
- BERT-base-Spanish-wwm-uncased for Spanish.

Training used class-weighted cross-entropy to counter label imbalance. The team also ran a bigram frequency analysis on the data.

On the official test set, the average scores were 0.6503 for Amharic, 0.6228 for Spanish and 0.5943 for English. The paper labels these numbers inconsistently: Pearson r in one table, F1 in another, and accuracy for Spanish in the abstract.

The main point is that a low-resource language (Amharic), paired with an Africa-centric encoder, can match or beat high-resource languages on fine-grained emotion intensity. Rare emotions and neighbouring intensity levels remain the hardest cases.

## Key points
- **Models and settings:** afro-xlmr-large-76L (Amharic), DeBERTa-v3-base (English), BERT-base-Spanish-wwm-uncased (Spanish), trained with Hugging Face Trainer, batch size 16, learning rate 2e-5, fp16 mixed precision and class-weighted CrossEntropyLoss.
- **Average test scores:** Amharic 0.6503, Spanish 0.6228, English 0.5943.
- **Best single emotion:** Amharic joy, with r = 0.7997. English surprise (0.4909) and anger (0.4761) were the weakest.
- **Skewed label distributions:**
  - English training data has no disgust examples at all, so no English disgust score is reported.
  - Fear dominates English (2573 training labels).
  - Amharic fear (145) and surprise (165) are rare.
- **Error analysis:**
  - Amharic sadness and fear were confused.
  - Spanish joy and surprise were mixed up.
  - English strong emotions written with subtle cues were underestimated as moderate.

## Interesting ideas
- Pick a separate, language- or region-specialised encoder for each language instead of one universal multilingual model; the Africa-centric encoder gave the best result on the lowest-resource language.
- Score an ordinal intensity scale (0–3) with Pearson correlation for each label, rather than exact-match accuracy, so that near misses count as partially right.
- Report scores per label and mark labels missing from the training data as unavailable rather than folding them into the average as zeros.
- Use error analysis that targets adjacent categories (sadness vs fear, joy vs surprise, strong vs moderate) to show where cues are too subtle for the model.
- Weight the loss by inverse class frequency so that rare labels such as surprise or disgust still shape training.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (tree): when gparse is extended to other languages or dialects, a specialised pretrained backbone could be compared against a generic multilingual one before committing to a single architecture.
- **I2** (learn): graded outputs such as confidence, degree adverbs or state intensity could be evaluated by correlation with gold grades, which rewards getting the order right.
- **I3** (learn): evaluation reports could flag frame or role types with no training support, so averages do not hide coverage holes.
- **I4** (frames): irony and speech-act detection in prag could log confusion pairs between neighbouring categories to decide where explicit rules or extra features are needed.
- **I5** (learn): when the parser or tagger is trained on silver data, rare dependency relations or morphological tags could be up-weighted in the same way to keep them from being swamped.

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

📚 Shelves (2): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works

🧭 Topics (3): [😊 Sentiment & emotion](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/) · 1,035 works · [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/) · 3,066 works · [🌍 Multilingual & cross-lingual NLP](https://md.research.elamur.ai/free/en/topics/multilingual-nlp/) · 882 works

## Cite

Cite as: Mikiyas Eyasu, Wendmnew Sitot Abebaw, Nida Hafeez, Fatima Uroosa, Tewodros Achamaleh Bizuneh, Grigori Sidorov and Alexander Gelbukh. 2025. Tewodros at SemEval-2025 Task 11: Multilingual Emotion Intensity Detection using Small Language Models. Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025).

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

```bibtex
@inproceedings{eyasu2025tewodros,
  title = {{Tewodros at SemEval-2025 Task 11: Multilingual Emotion Intensity Detection using Small Language Models}},
  author = {Mikiyas Eyasu, Wendmnew Sitot Abebaw, Nida Hafeez, Fatima Uroosa, Tewodros Achamaleh Bizuneh, Grigori Sidorov and Alexander Gelbukh},
  booktitle = {Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)},
  year = {2025},
  url = {https://aclanthology.org/2025.semeval-1.196/}
}
```

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## References

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*Updated 2 November 2026*