# SignBank+: Preparing a Multilingual Sign Language Dataset for Machine Translation Using Large Language Models

arXiv: https://arxiv.org/abs/2309.11566 · HTML: https://mlsanity.com/paper/2309.11566/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 27 references that are in the map.
- First posted on arXiv 20 Sep 2023.

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

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation](https://mlsanity.com/paper/1611.04558/) (2016)
- [Revisiting Low-Resource Neural Machine Translation: A Case Study](https://mlsanity.com/paper/1905.11901/) (2019)
- [Dual Conditional Cross-Entropy Filtering of Noisy Parallel Corpora](https://mlsanity.com/paper/1809.00197/) (2018)
- [Joey NMT: A Minimalist NMT Toolkit for Novices](https://mlsanity.com/paper/1907.12484/) (2019)
- [Machine Translation between Spoken Languages and Signed Languages Represented in SignWriting](https://mlsanity.com/paper/2210.05404/) (2022)
- [Sockeye: A Toolkit for Neural Machine Translation](https://mlsanity.com/paper/1712.05690/) (2017)
- [Changing the Representation: Examining Language Representation for Neural Sign Language Production](https://mlsanity.com/paper/2210.06312/) (2022)

## Abstract (arXiv)

We introduce SignBank+, a clean version of the SignBank dataset, optimized for machine translation between spoken language text and SignWriting, a phonetic sign language writing system. In addition to previous work that employs complex factorization techniques to enable translation between text and SignWriting, we show that a traditional text-to-text translation approach performs equally effectively on the cleaned SignBank+ dataset. Our evaluation results indicate that models trained on SignBank+ surpass those on the original dataset, establishing a new benchmark for SignWriting-based sign language translation and providing an open resource for future research.