Introducing A Bangla Sentence - Gloss Pair Dataset for Bangla Sign Language Translation and Research

• URL: http://arxiv.org/abs/2511.08507v1
• Date: Wed, 12 Nov 2025 02:02:07 GMT
• Title: Introducing A Bangla Sentence - Gloss Pair Dataset for Bangla Sign Language Translation and Research
• Authors: Neelavro Saha, Rafi Shahriyar, Nafis Ashraf Roudra, Saadman Sakib, Annajiat Alim Rasel,
• Abstract summary: We introduce Bangla-SGP, a novel parallel dataset consisting of 1,000 human-annotated sentence-gloss pairs.<n>Our dataset consists of 1000 high quality Bangla sentences that are manually annotated into a gloss sequence by a professional signer.
• Score: 0.4077787659104315
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Bangla Sign Language (BdSL) translation represents a low-resource NLP task due to the lack of large-scale datasets that address sentence-level translation. Correspondingly, existing research in this field has been limited to word and alphabet level detection. In this work, we introduce Bangla-SGP, a novel parallel dataset consisting of 1,000 human-annotated sentence-gloss pairs which was augmented with around 3,000 synthetically generated pairs using syntactic and morphological rules through a rule-based Retrieval-Augmented Generation (RAG) pipeline. The gloss sequences of the spoken Bangla sentences are made up of individual glosses which are Bangla sign supported words and serve as an intermediate representation for a continuous sign. Our dataset consists of 1000 high quality Bangla sentences that are manually annotated into a gloss sequence by a professional signer. The augmentation process incorporates rule-based linguistic strategies and prompt engineering techniques that we have adopted by critically analyzing our human annotated sentence-gloss pairs and by working closely with our professional signer. Furthermore, we fine-tune several transformer-based models such as mBart50, Google mT5, GPT4.1-nano and evaluate their sentence-to-gloss translation performance using BLEU scores, based on these evaluation metrics we compare the model's gloss-translation consistency across our dataset and the RWTH-PHOENIX-2014T benchmark.

Related papers

• Continuous Bangla Sign Language Translation: Mitigating the Expense of Gloss Annotation with the Assistance of Graph [0.8192907805418583]
  In societies that prioritize spoken languages, sign language often faces underestimation, leading to social exclusion.<n>The Continuous Bangla Sign Language Translation project aims to address this gap by enhancing translation methods.<n>Our contributions include architectural fusion, exploring various fusion strategies, and achieving a new state-of-the-art performance on diverse sign language datasets.
  arXiv  Detail & Related papers  (2025-08-14T14:32:31Z)
• SLRTP2025 Sign Language Production Challenge: Methodology, Results, and Future Work [87.9341538630949]
  The first Sign Language Production Challenge was held as part of the third SLRTP Workshop at CVPR 2025.<n>The competition's aims are to evaluate architectures that translate from spoken language sentences to a sequence of skeleton poses.<n>This paper presents the challenge design and the winning methodologies.
  arXiv  Detail & Related papers  (2025-08-09T11:57:33Z)
• Bridging Sign and Spoken Languages: Pseudo Gloss Generation for Sign Language Translation [48.20483623444857]
  Sign Language Translation aims to map sign language videos to spoken language text.<n>A common approach relies on gloss annotations as an intermediate representation.<n>We propose a gloss-free pseudo gloss generation framework that eliminates the need for human-annotated glosses.
  arXiv  Detail & Related papers  (2025-05-21T12:19:55Z)
• Lost in Translation, Found in Context: Sign Language Translation with Contextual Cues [56.038123093599815]
  Our objective is to translate continuous sign language into spoken language text.<n>We incorporate additional contextual cues together with the signing video.<n>We show that our contextual approach significantly enhances the quality of the translations.
  arXiv  Detail & Related papers  (2025-01-16T18:59:03Z)
• Gloss-free Sign Language Translation: Improving from Visual-Language Pretraining [56.26550923909137]
  Gloss-Free Sign Language Translation (SLT) is a challenging task due to its cross-domain nature. We propose a novel Gloss-Free SLT based on Visual-Language Pretraining (GFSLT-) Our approach involves two stages: (i) integrating Contrastive Language-Image Pre-training with masked self-supervised learning to create pre-tasks that bridge the semantic gap between visual and textual representations and restore masked sentences, and (ii) constructing an end-to-end architecture with an encoder-decoder-like structure that inherits the parameters of the pre-trained Visual and Text Decoder from
  arXiv  Detail & Related papers  (2023-07-27T10:59:18Z)
• On Evaluation of Bangla Word Analogies [0.8658596218544772]
  This paper presents a high-quality dataset for evaluating the quality of Bangla word embeddings. Despite being the 7th most-spoken language in the world, Bangla is a low-resource language and popular NLP models fail to perform well.
  arXiv  Detail & Related papers  (2023-04-10T14:27:35Z)
• BanglaParaphrase: A High-Quality Bangla Paraphrase Dataset [3.922582192616519]
  We present BanglaParaphrase, a high-quality synthetic Bangla Paraphrase dataset curated by a novel filtering pipeline. We aim to take a step towards alleviating the low resource status of the Bangla language in the NLP domain through the introduction of BanglaParaphrase.
  arXiv  Detail & Related papers  (2022-10-11T02:52:31Z)
• Changing the Representation: Examining Language Representation for Neural Sign Language Production [43.45785951443149]
  We apply Natural Language Processing techniques to the first step of the Neural Sign Language Production pipeline. We use language models such as BERT and Word2Vec to create better sentence level embeddings. We introduce Text to HamNoSys (T2H) translation, and show the advantages of using a phonetic representation for sign language translation.
  arXiv  Detail & Related papers  (2022-09-16T12:45:29Z)
• Improving Sign Language Translation with Monolingual Data by Sign Back-Translation [105.83166521438463]
  We propose a sign back-translation (SignBT) approach, which incorporates massive spoken language texts into sign training. With a text-to-gloss translation model, we first back-translate the monolingual text to its gloss sequence. Then, the paired sign sequence is generated by splicing pieces from an estimated gloss-to-sign bank at the feature level.
  arXiv  Detail & Related papers  (2021-05-26T08:49:30Z)
• Data Augmentation for Sign Language Gloss Translation [115.13684506803529]
  Sign language translation (SLT) is often decomposed into video-to-gloss recognition and gloss-totext translation. We focus here on gloss-to-text translation, which we treat as a low-resource neural machine translation (NMT) problem. By pre-training on the thus obtained synthetic data, we improve translation from American Sign Language (ASL) to English and German Sign Language (DGS) to German by up to 3.14 and 2.20 BLEU, respectively.
  arXiv  Detail & Related papers  (2021-05-16T16:37:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.