---
title: "Cross-Lingual Emotion Recognition in Balinese Text using"
description: "The paper addresses cross-lingual emotion recognition (CLER) for Balinese, an Austronesian language… 53 references, cited by 1 paper here."
authors: ["Putu Kussa Laksana Utama", "Jilles Steeve Dibangoye", "Tsegaye Misikir Tashu"]
year: 2026
venue: "Proceedings of the Second Workshop on Language Models for Low-Resource Languages (LoResLM 2026)"
source: "https://aclanthology.org/2026.loreslm-1.21/"
doi: "10.18653/v1/2026.loreslm-1.21"
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/utama-2026-cross-lingual-emotion-recognition-balinese/"
---
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# Cross-Lingual Emotion Recognition in Balinese Text using Multilingual-LLMs under Peer-Collaborations Settings

**Authors:** Putu Kussa Laksana Utama, Jilles Steeve Dibangoye and Tsegaye Misikir Tashu · **Year:** 2026 · **Venue:** [Proceedings of the Second Workshop on Language Models for Low-Resource Languages (LoResLM 2026)](https://md.research.elamur.ai/free/en/venues/loreslm/) ([Workshop on Language Models for Low-Resource Languages 2026](https://md.research.elamur.ai/free/en/venues/loreslm/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.loreslm-1.21/) · [PDF](https://aclanthology.org/2026.loreslm-1.21.pdf) · [DOI 10.18653/v1/2026.loreslm-1.21](https://doi.org/10.18653/v1/2026.loreslm-1.21) · [OpenAlex](https://openalex.org/W7140115755)

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

## Summary
The paper addresses cross-lingual emotion recognition (CLER) for Balinese, an Austronesian language with almost no labelled data. Earlier multilingual encoders handle such languages poorly. The authors built the first Balinese emotion benchmark, a multi-label corpus. Experts in Balinese linguistics and psychology annotated it using Ekman's six basic emotions. They also propose Multi-Agent Peer Collaboration (MAPC), which splits multi-label prediction into one binary decision per emotion. Several different LLMs make initial judgements, critique and revise each other's answers, and then reach a majority vote. Few-shot prompts use examples from related languages (Indonesian, Javanese, Sundanese) taken from BRIGHTER. LLMs clearly beat the LaBSE encoder. MAPC achieved the best overall macro-F1 of 63.95, ahead of every single-model baseline in both zero-shot and few-shot settings. The gain over the strongest single model is small and not statistically significant per category. Still, the work provides a public resource and shows that collaborating LLMs can help ultra-low-resource languages.

## Key points
- **Dataset:** BaliEmoV1 is released on Hugging Face (`kyo21/BaliEmoV1`). It is multi-label and covers anger, disgust, fear, joy, sadness and surprise.
- **Models compared:** LaBSE versus Qwen3-32b, Kimi K2-Instruct-0905 and Llama-3.3-70b, evaluated with macro-F1.
- **Model size and prompting:** Larger models did not adapt better to few-shot prompting.
  - Llama-3.3-70b led in zero-shot (63.34) but fell to 58.29 with few-shot examples.
  - Qwen3-32b rose to 63.81 with few-shot examples.
- **Effect of review and revision:** Each agent's macro-F1 improved:
  - Qwen3-32b: 63.52 → 67.72
  - Kimi K2: 40.13 → 62.47
  - Llama-3.3: 57.43 → 61.66
- **Significance and limitations:**
  - McNemar's tests were not significant at p \< 0.05 for any emotion; anger came closest (p = 0.07).
  - Majority voting can lock in errors that most agents share.
  - Running multiple agents raises computational cost and latency.

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

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## Cite

Cite as: Putu Kussa Laksana Utama, Jilles Steeve Dibangoye and Tsegaye Misikir Tashu. 2026. Cross-Lingual Emotion Recognition in Balinese Text using Multilingual-LLMs under Peer-Collaborations Settings. Proceedings of the Second Workshop on Language Models for Low-Resource Languages (LoResLM 2026).

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

```bibtex
@inproceedings{utama2026cross,
  title = {{Cross-Lingual Emotion Recognition in Balinese Text using Multilingual-LLMs under Peer-Collaborations Settings}},
  author = {Putu Kussa Laksana Utama, Jilles Steeve Dibangoye and Tsegaye Misikir Tashu},
  booktitle = {Proceedings of the Second Workshop on Language Models for Low-Resource Languages (LoResLM 2026)},
  year = {2026},
  doi = {10.18653/v1/2026.loreslm-1.21},
  url = {https://doi.org/10.18653/v1/2026.loreslm-1.21}
}
```

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43. Jakub Šmíd, Pavel Přibáň, and Pavel Král. 2025. Advancing cross-lingual aspect-based sentiment analysis with llms and constrained decoding for sequence-to-sequence models. International Conference on Agents and Artificial Intelligence, 2:757–766. [find](https://scholar.google.com/scholar?q=Jakub+%C5%A0m%C3%ADd%2C+Pavel+Pr%CC%8Cib%C3%A1n%CC%8C%2C+and+Pavel+Kr%C3%A1l%2E+2025%2E+Advancing+cross%2Dlingual+aspect%2Dbased+sentiment+analysis+with+llms+and+constrained+decoding+for+sequence%2Dto%2Dsequence+models%2E+International+Conference+o)
44. Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed H. Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus. 2022. [Emergent abilities of large language models](https://md.research.elamur.ai/free/en/papers/wei-2022-emergent-abilities-large-language-models/).
45. Trans. Mach. Learn. Res., 2022. [find](https://scholar.google.com/scholar?q=Trans%2E+Mach%2E+Learn%2E+Res%2E%2C+2022%2E)
46. Feng Xiong, Jun Wang, Geng Tu, and Ruifeng Xu. 2024. Hitsz-hlt at wassa-2024 shared task 2: Language-agnostic multi-task learning for explainability of cross-lingual emotion detection. [find](https://scholar.google.com/scholar?q=Feng+Xiong%2C+Jun+Wang%2C+Geng+Tu%2C+and+Ruifeng+Xu%2E+2024%2E+Hitsz%2Dhlt+at+wassa%2D2024+shared+task+2%3A+Language%2Dagnostic+multi%2Dtask+learning+for+explainability+of+cross%2Dlingual+emotion+detection%2E)
47. WASSA 2024 - 14th Workshop on Computational Approaches to Subjectivity, Sentiment, and Social Media Analysis, Proceedings of the Workshop, pages 476–482. [find](https://scholar.google.com/scholar?q=WASSA+2024+%2D+14th+Workshop+on+Computational+Approaches+to+Subjectivity%2C+Sentiment%2C+and+Social+Media+Analysis%2C+Proceedings+of+the+Workshop%2C+pages+476%E2%80%93482%2E)
48. Zhenran Xu, Senbao Shi, Baotian Hu, Jindi Yu, Dongfang Li, Min Zhang, and Yuxiang Wu. 2023. [Towards reasoning in large language models via multi-agent peer review collaboration](https://md.research.elamur.ai/free/en/papers/xu-2023-towards-reasoning-large-language-models/). Preprint, arXiv:2311.08152.
49. Jieying Xue, Phuong Minh Nguyen, Minh Le Nguyen, and Xin Liu. 2025a. JNLP at SemEval-2025 Task 11: Cross-Lingual Multi-Label Emotion Detection Using Generative Models. pages 20–27. [find](https://scholar.google.com/scholar?q=Jieying+Xue%2C+Phuong+Minh+Nguyen%2C+Minh+Le+Nguyen%2C+and+Xin+Liu%2E+2025a%2E+JNLP+at+SemEval%2D2025+Task+11%3A+Cross%2DLingual+Multi%2DLabel+Emotion+Detection+Using+Generative+Models%2E+pages+20%E2%80%9327%2E)
50. Jieying Xue, Phuong Minh Nguyen, Minh Le Nguyen, and Xin Liu. 2025b. Jnlp at semeval-2025 task 11: Cross-lingual multi-label emotion detection using generative models. [find](https://scholar.google.com/scholar?q=Jieying+Xue%2C+Phuong+Minh+Nguyen%2C+Minh+Le+Nguyen%2C+and+Xin+Liu%2E+2025b%2E+Jnlp+at+semeval%2D2025+task+11%3A+Cross%2Dlingual+multi%2Dlabel+emotion+detection+using+generative+models%2E)
51. Daniel Yohanes, Jessen Surya Putra, Kenneth Filbert, Kristien Margi Suryaningrum, and Hanis Amalia Saputri. 2023. Emotion detection in textual data using deep learning. Procedia Computer Science, 227:464– 473. [find](https://scholar.google.com/scholar?q=Daniel+Yohanes%2C+Jessen+Surya+Putra%2C+Kenneth+Filbert%2C+Kristien+Margi+Suryaningrum%2C+and+Hanis+Amalia+Saputri%2E+2023%2E+Emotion+detection+in+textual+data+using+deep+learning%2E+Procedia+Computer+Science%2C+227%3A)
52. Ruohong Zhang, Yau Shian Wang, Yiming Yang, Donghan Yu, Tom Vu, and Likun Lei. 2023. Long-tailed extreme multi-label text classification by the retrieval of generated pseudo label descriptions. [find](https://scholar.google.com/scholar?q=Ruohong+Zhang%2C+Yau+Shian+Wang%2C+Yiming+Yang%2C+Donghan+Yu%2C+Tom+Vu%2C+and+Likun+Lei%2E+2023%2E+Long%2Dtailed+extreme+multi%2Dlabel+text+classification+by+the+retrieval+of+generated+pseudo+label+descriptions%2E)
53. EACL 2023 - 17th Conference of the European Chapter of the *Figure 1: Few-shot prompt example of assessing emotion fear.* *Figure 2: Review prompt example of assesing emotion fear.* *Figure 3: Revision prompt example of assesing emotion fear.* [find](https://scholar.google.com/scholar?q=EACL+2023+%2D+17th+Conference+of+the+European+Chapter+of+the+%2AFigure+1%3A+Few%2Dshot+prompt+example+of+assessing+emotion+fear%2E%2A+%2AFigure+2%3A+Review+prompt+example+of+assesing+emotion+fear%2E%2A+%2AFigure+3%3A+Revisio)

*Updated 2 November 2026*