# Breaking the Silence: A Dataset and Benchmark for Bangla Text-to-Gloss Translation

arXiv: https://arxiv.org/abs/2504.02293 · HTML: https://mlsanity.com/paper/2504.02293/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 39 references that are in the map.
- First posted on arXiv 3 Apr 2025.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [Efficacy of Synthetic Data as a Benchmark](https://mlsanity.com/paper/2409.11968/) (2024)
- [Data Augmentation for Sign Language Gloss Translation](https://mlsanity.com/paper/2105.07476/) (2021)
- [A Data-Driven Representation for Sign Language Production](https://mlsanity.com/paper/2404.11499/) (2024)
- [BanglaBook: A Large-scale Bangla Dataset for Sentiment Analysis from Book Reviews](https://mlsanity.com/paper/2305.06595/) (2023)
- [Hybrid Training Approaches for LLMs: Leveraging Real and Synthetic Data to Enhance Model Performance in Domain-Specific Applications](https://mlsanity.com/paper/2410.09168/) (2024)
- [Human Interest or Conflict? Leveraging LLMs for Automated Framing Analysis in TV Shows](https://mlsanity.com/paper/2409.12561/) (2024)
- [BanglaLlama: LLaMA for Bangla Language](https://mlsanity.com/paper/2410.21200/) (2024)
- [Advancing Text-to-GLOSS Neural Translation Using a Novel Hyper-parameter Optimization Technique](https://mlsanity.com/paper/2309.02162/) (2023)
- [Investigating Cost-Efficiency of LLM-Generated Training Data for Conversational Semantic Frame Analysis](https://mlsanity.com/paper/2410.06550/) (2024)

## Built on by (most cited first, 3 of 3)

- [Using Sign Language Production as Data Augmentation to enhance Sign Language Translation](https://mlsanity.com/paper/2506.09643/) (2025)
- [Stable Signer: Hierarchical Sign Language Generative Model](https://mlsanity.com/paper/2512.04048/) (2025)
- [Corpus Augmentation for Sign Language Translation via LLM-Guided Video Stitching](https://mlsanity.com/paper/2606.11925/) (2026)

## Abstract (arXiv)

Gloss is a written approximation that bridges Sign Language (SL) and its corresponding spoken language. Despite a deaf and hard-of-hearing population of at least 3 million in Bangladesh, Bangla Sign Language (BdSL) remains largely understudied, with no prior work on Bangla text-to-gloss translation and no publicly available datasets. To address this gap, we construct the first Bangla text-to-gloss dataset, consisting of 1,000 manually annotated and 4,000 synthetically generated Bangla sentence-gloss pairs, along with 159 expert human-annotated pairs used as a test set. Our experimental framework performs a comparative analysis between several fine-tuned open-source models and a leading closed-source LLM to evaluate their performance in low-resource BdSL translation. GPT-5.4 achieves the best overall performance, while a fine-tuned mBART model performs competitively despite being approximately 100% smaller. Qwen-3 outperforms all other models in human evaluation. This work introduces the first dataset and trained model for Bangla text-to-gloss translation. It also demonstrates the effectiveness of systematically generated synthetic data for addressing challenges in low-resource sign language translation.