---
title: "Bangla Key2Text — Talukder 2026"
description: "Bangla is among the world's most widely spoken languages, but no earlier study or training resource existed for producing Bangla sentences from a set of…"
authors: ["Tonmoy Talukder", "G M Shahariar"]
year: 2026
venue: "Proceedings of the Fifteenth Language Resources and Evaluation Conference"
source: "https://aclanthology.org/2026.lrec-1.303/"
doi: "10.63317/4wkpwaxktwfn"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/talukder-2026-bangla-key2text-text-generation-keywords/"
---
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# Bangla Key2Text: Text Generation from Keywords for a Low Resource Language

**Authors:** Tonmoy Talukder, G M Shahariar · **Year:** 2026 · **Venue:** Proceedings of the Fifteenth Language Resources and Evaluation Conference · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.lrec-1.303/) · [PDF](https://aclanthology.org/2026.lrec-1.303.pdf) · [DOI 10.63317/4wkpwaxktwfn](https://doi.org/10.63317/4wkpwaxktwfn) · [OpenAlex](https://openalex.org/W7160105810)

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

## Summary
Bangla is among the world's most widely spoken languages, but no earlier study or training resource existed for producing Bangla sentences from a set of unordered keywords. The authors address this with Bangla Key2Text, a corpus of 2.6 million keyword–text pairs. They sampled texts from the Bangla Newspaper Dataset, removed markup and special characters, and applied an automatic extractor built on BanglaBERT. The extractor assigns each word an importance score based on context and keeps the highest-scoring ones. They selected it after comparing it with other neural and classic extraction baselines on a manually annotated subset. They then fine-tuned mT5 and BanglaT5 on the pairs as reference systems. Evaluation used automatic metrics, human ratings, unseen and dialectal inputs, and a comparison with several 4-bit quantized open LLMs prompted zero-shot. The small fine-tuned models outperformed the general-purpose LLMs across the board. This shows that task-specific training pays off for a low-resource language. The public release of the data, code and models gives Bangla NLG its first benchmark for this task.

## Key points
- Source corpus: Bangla Newspaper Dataset (Nazi, 2020), with 409.5K articles. The 2.6M texts are split into 2M for training, 500K for validation and 100K for testing.
- 10K test texts received manual keyword annotation. Two annotators labeled each text independently, and a third chose the final set. This subset was used to evaluate the extractor and to select a decoding strategy.
- Each text has on average about 9.4 extracted keywords and about 15.1 words. The keyword-to-text length ratio is roughly 0.60.
- Baseline models are mT5 and BanglaT5. Their BERTScores are 91.06% (mT5) and 91.51% (BanglaT5). Both beat zero-shot quantized LLaMA, Phi, Gemma, Mistral and Qwen models.
- The paper reports several observations and limitations:
  - When given mixed Bangla–English keywords, the models still output Bangla, showing cross-lingual transfer.
  - Changing the keyword order alters sentence structure but mostly preserves meaning.
  - Some keywords are occasionally omitted, and constrained beam search reduced output quality.
  - Because the data comes from news, the models may handle informal Bangla less well.

*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/en/shelves/linguistics/)

Topics: [Text generation](https://md.research.elamur.ai/free/en/topics/text-generation/) · [Low-resource languages](https://md.research.elamur.ai/free/en/topics/low-resource-languages/)

## Cite

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

```bibtex
@inproceedings{talukder2026bangla,
  title = {{Bangla Key2Text: Text Generation from Keywords for a Low Resource Language}},
  author = {Talukder, Tonmoy and Shahariar, G M},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/4wkpwaxktwfn},
  url = {https://doi.org/10.63317/4wkpwaxktwfn}
}
```

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