---
title: "CAI@LTEDI 2026 — paper by Nair et al. (2026)"
description: "The paper addresses rewriting gender-biased or gender-marked sentences into neutral versions that keep the original meaning. 14 references."
authors: ["Aiswariya p Nair", "Sree S Bhagya", "Chinnu Jacob"]
year: 2026
venue: "Proceedings of the Sixth Workshop on Language Technology for Equality, Diversity, Inclusion"
source: "https://aclanthology.org/2026.ltedi-1.14/"
doi: "10.18653/v1/2026.ltedi-1.14"
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/nair-2026-cai-ltedi-2026-multilingual-gender/"
---
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# CAI@LTEDI 2026: Multilingual Gender Inclusive Language Generation using Instruction-Guided mT5 Transformer Model

**Authors:** Aiswariya p Nair, Sree S Bhagya and Chinnu Jacob · **Year:** 2026 · **Venue:** [Proceedings of the Sixth Workshop on Language Technology for Equality, Diversity, Inclusion](https://md.research.elamur.ai/free/en/venues/ltedi/) ([Workshop on Language Technology for Equality, Diversity, Inclusion 2026](https://md.research.elamur.ai/free/en/venues/ltedi/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.ltedi-1.14/) · [PDF](https://aclanthology.org/2026.ltedi-1.14.pdf) · [DOI 10.18653/v1/2026.ltedi-1.14](https://doi.org/10.18653/v1/2026.ltedi-1.14) · [OpenAlex](https://openalex.org/W7166826319)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary

The paper addresses rewriting gender-biased or gender-marked sentences into neutral versions that keep the original meaning. The work is part of the LT-EDI 2026 shared task, which covers English, German, Spanish, Tamil, and Kannada. Team CAI merged the organisers' parallel datasets into one corpus and fine-tuned a single mT5-small sequence-to-sequence model on it, marking every input with a "rewrite:" task prefix. During inference, they used beam search with repetition constraints, because greedy decoding had produced truncated, repetitive, or language-mixed output, especially for Tamil and Kannada. Evaluation combined a fairness index (GIFI), embedding-based semantic similarity, and a length term. In the official ranking, the system placed 4th for German, Spanish, and Kannada, 5th for Tamil, and 7th for English. Error analysis found truncation, repetition, over-neutralization, and code-mixing. The study matters as a low-cost baseline: one compact model can attempt inclusive rewriting across typologically different languages, including Dravidian ones, though its limits are clear.

## Key points

- Training data (Subtask A) consists of sentence pairs: 1074 each for English, Tamil, and Kannada, 1002 for German, and only 200 for Spanish.
- The fine-tuning setup was mT5-small with AdamW, learning rate 3e-5, batch size 8, 5 epochs, max sequence length 128, and beam width 4, using HuggingFace Transformers. mT5-base was not tried because of compute limits.
- The metrics were defined as follows:
  - GIFI = 0.6·bias removal + 0.4·inclusive term usage.
  - Overall score = 0.5·GIFI + 0.4·similarity + 0.1·length.
- Official average scores per language were Kannada 64.00, English 56.88, Tamil 50.68, Spanish 45.83, and German 14.71. Two languages scored 0 on the QR metric: Kannada and Spanish scored 5.00 and German scored 0.00 on that column.
- The authors suggest future work on larger datasets, better decoding, morphology-aware modelling, and discourse context, since the current method is sentence-level only.

*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

Also on the byline: [TKM College of Engineering](https://ror.org/013vqm712).

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## Cite

Cite as: Aiswariya p Nair, Sree S Bhagya and Chinnu Jacob. 2026. CAI@LTEDI 2026: Multilingual Gender Inclusive Language Generation using Instruction-Guided mT5 Transformer Model. Proceedings of the Sixth Workshop on Language Technology for Equality, Diversity, Inclusion.

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

```bibtex
@inproceedings{nair2026cai,
  title = {{CAI@LTEDI 2026: Multilingual Gender Inclusive Language Generation using Instruction-Guided mT5 Transformer Model}},
  author = {Aiswariya p Nair, Sree S Bhagya and Chinnu Jacob},
  booktitle = {Proceedings of the Sixth Workshop on Language Technology for Equality, Diversity, Inclusion},
  year = {2026},
  doi = {10.18653/v1/2026.ltedi-1.14},
  url = {https://doi.org/10.18653/v1/2026.ltedi-1.14}
}
```

- [← previous Caged Birds and Cute Bookworms: Feminine Tropes and Implicit Gender Bias in Large Language Models](https://md.research.elamur.ai/free/en/papers/nishal-2026-caged-birds-cute-bookworms-feminine/)
- [next → CAIR: Causal Adaptive Information-based Reinforcement Learning for Multimodal Emotion Reasoning](https://md.research.elamur.ai/free/en/papers/zhang-2026-cair-causal-adaptive-information-based/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/25/)

## References

14 references, 4 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Faezeh Torabi Asr, Maite Taboada, and Alexandra J. Y. Cheng. 2021. The gender gap tracker: Using natural language processing to measure gender bias in media. PLOS ONE. [find](https://scholar.google.com/scholar?q=Faezeh+Torabi+Asr%2C+Maite+Taboada%2C+and+Alexandra+J%2E+Y%2E+Cheng%2E+2021%2E+The+gender+gap+tracker%3A+Using+natural+language+processing+to+measure+gender+bias+in+media%2E+PLOS+ONE%2E)
2. Marion Bartl, Malvina Nissim, and Albert Gatt. 2020. [Unmasking contextual stereotypes: Measuring and mitigating bert’s gender bias](https://md.research.elamur.ai/free/en/papers/bartl-2020-unmasking-contextual-stereotypes-measuring-mitigating/). In ACL Workshop on Gender Bias in NLP.
3. Tolga Bolukbasi, Kai-Wei Chang, James Zou, Venkatesh Saligrama, and Adam Kalai. 2016. [Man is to computer programmer as woman is to homemaker? debiasing word embeddings](https://md.research.elamur.ai/free/en/papers/bolukbasi-2016-man-computer-programmer-woman-homemaker/). In Advances in Neural Information Processing Systems.
4. Bharathi Raja Chakravarthi, Shunmuga Priya Muthusamy Chinnan, Paul Buitelaar, and Meghann Drury-Grogan. 2026a. Insights from multilingual gender inclusive language generation shared task. In Proceedings of the Sixth Workshop on Language Technology for Equality, Diversity, Inclusion. [find](https://scholar.google.com/scholar?q=Bharathi+Raja+Chakravarthi%2C+Shunmuga+Priya+Muthusamy+Chinnan%2C+Paul+Buitelaar%2C+and+Meghann+Drury%2DGrogan%2E+2026a%2E+Insights+from+multilingual+gender+inclusive+language+generation+shared+task%2E+In+Proceedin)
5. Bharathi Raja Chakravarthi, Shunmuga Priya Muthusamy Chinnan, Paul Buitelaar, Meghann L. Drury-Grogan, Miguel Ángel García Cumbreras, Salud María Jiménez Zafra, Thomas Mandl, Sylvia Jaki, Rahul Ponnusamy, Anand Kumar M, Dhanalakshmi V, Bharathi B, Premjith B, Senthil Kumar B, and Sathiyaraj T. 2026b… [find](https://scholar.google.com/scholar?q=Bharathi+Raja+Chakravarthi%2C+Shunmuga+Priya+Muthusamy+Chinnan%2C+Paul+Buitelaar%2C+Meghann+L%2E+Drury%2DGrogan%2C+Miguel+%C3%81ngel+Garc%C3%ADa+Cumbreras%2C+Salud+Mar%C3%ADa+Jim%C3%A9nez+Zafra%2C+Thomas+Mandl%2C+Sylvia+Jaki%2C+Rahul+Ponnus)
6. Emily Dinan, Angela Fan, Adina Williams, Jack Urbanek, Douwe Kiela, and Jason Weston. 2020. [Queens are powerful too: Mitigating gender bias in dialogue generation](https://md.research.elamur.ai/free/en/papers/dinan-2020-queens-are-powerful-too-mitigating/). In Proceedings of EMNLP.
7. Enzo Doyen and Amalia Todirascu. 2025. Genre: A french gender-neutral rewriting system using collective nouns. In Findings of the Association for Computational Linguistics: ACL 2025, pages 7889–7909. [find](https://scholar.google.com/scholar?q=Enzo+Doyen+and+Amalia+Todirascu%2E+2025%2E+Genre%3A+A+french+gender%2Dneutral+rewriting+system+using+collective+nouns%2E+In+Findings+of+the+Association+for+Computational+Linguistics%3A+ACL+2025%2C+pages+7889%E2%80%937909%2E)
8. Dirk Hovy and Shannon L. Spruit. 2021. The social impact of natural language processing. Computational Linguistics. [find](https://scholar.google.com/scholar?q=Dirk+Hovy+and+Shannon+L%2E+Spruit%2E+2021%2E+The+social+impact+of+natural+language+processing%2E+Computational+Linguistics%2E)
9. Paul Lerner and Cyril Grouin. 2024. Inclure: a dataset and toolkit for inclusive french translation. In Proceedings of the 17th Workshop on Building and Using Comparable Corpora (BUCC)@ LREC-COLING 2024, pages 59–68. [find](https://scholar.google.com/scholar?q=Paul+Lerner+and+Cyril+Grouin%2E+2024%2E+Inclure%3A+a+dataset+and+toolkit+for+inclusive+french+translation%2E+In+Proceedings+of+the+17th+Workshop+on+Building+and+Using+Comparable+Corpora+%28BUCC%29%40+LREC%2DCOLING+20)
10. Haochen Liu, Wentao Wang, Yiqi Wang, Hui Liu, Zitao Liu, and Jiliang Tang. 2020. Mitigating gender bias for neural dialogue generation with adversarial learning. In Proceedings of EMNLP. [find](https://scholar.google.com/scholar?q=Haochen+Liu%2C+Wentao+Wang%2C+Yiqi+Wang%2C+Hui+Liu%2C+Zitao+Liu%2C+and+Jiliang+Tang%2E+2020%2E+Mitigating+gender+bias+for+neural+dialogue+generation+with+adversarial+learning%2E+In+Proceedings+of+EMNLP%2E)
11. Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, and Nanyun Peng. 2019. [The woman worked as a babysitter: On biases in language generation](https://md.research.elamur.ai/free/en/papers/sheng-2019-woman-worked-as-babysitter-biases/). In Proceedings of EMNLP.
12. Karolina Stańczak and Isabelle Augenstein. 2021. A survey on gender bias in natural language processing. Journal of the ACM. [find](https://scholar.google.com/scholar?q=Karolina+Stan%CC%81czak+and+Isabelle+Augenstein%2E+2021%2E+A+survey+on+gender+bias+in+natural+language+processing%2E+Journal+of+the+ACM%2E)
13. Leonor Veloso, Luisa Coheur, and Rui Ribeiro. 2023. A rewriting approach for gender inclusivity in portuguese. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 8747–8759. [find](https://scholar.google.com/scholar?q=Leonor+Veloso%2C+Luisa+Coheur%2C+and+Rui+Ribeiro%2E+2023%2E+A+rewriting+approach+for+gender+inclusivity+in+portuguese%2E+In+Findings+of+the+Association+for+Computational+Linguistics%3A+EMNLP+2023%2C+pages+8747%E2%80%938759)
14. Shunmuga Priya Muthusamy Chinnan, Meghann Drury-Grogan, and Bharathi Raja Chakravarthi. 2025. Gender inclusive language generation framework: A rea-https://github.com/123veno/LTEDI_ 2026-GENDER-INCLUSIVE-LANGUAGE-GENERATION [find](https://scholar.google.com/scholar?q=Shunmuga+Priya+Muthusamy+Chinnan%2C+Meghann+Drury%2DGrogan%2C+and+Bharathi+Raja+Chakravarthi%2E+2025%2E+Gender+inclusive+language+generation+framework%3A+A+rea%2Dhttps%3A%2F%2Fgithub%2Ecom%2F123veno%2FLTEDI%5F+2026%2DGENDER%2DINCLUS)

*Updated 1 November 2026*