# DiscoSign: Discourse-Aware Text to Sign Language Gloss Translation

arXiv: https://arxiv.org/abs/2609.02796 · HTML: https://mlsanity.com/paper/2609.02796/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 37 references that are in the map.
- First posted on arXiv 2 Sep 2026.

## Builds on (most cited first, 18 of 18)

- [COMET: A Neural Framework for MT Evaluation](https://mlsanity.com/paper/2009.09025/) (2020)
- [LLMs are Good Sign Language Translators](https://mlsanity.com/paper/2404.00925/) (2024)
- [Including Signed Languages in Natural Language Processing](https://mlsanity.com/paper/2105.05222/) (2021)
- [Sign2GPT: Leveraging Large Language Models for Gloss-Free Sign Language Translation](https://mlsanity.com/paper/2405.04164/) (2024)
- [Neural Sign Actors: A diffusion model for 3D sign language production from text](https://mlsanity.com/paper/2312.02702/) (2023)
- [Considerations for meaningful sign language machine translation based on glosses](https://mlsanity.com/paper/2211.15464/) (2022)
- [Shortcomings of LLMs for Low-Resource Translation: Retrieval and Understanding are Both the Problem](https://mlsanity.com/paper/2406.15625/) (2024)
- [Including Facial Expressions in Contextual Embeddings for Sign Language Generation](https://mlsanity.com/paper/2202.05383/) (2022)
- [ASL Video Corpora & Sign Bank: Resources Available through the American Sign Language Linguistic Research Project (ASLLRP)](https://mlsanity.com/paper/2201.07899/) (2022)
- [Modeling Intensification for Sign Language Generation: A Computational Approach](https://mlsanity.com/paper/2203.09679/) (2022)
- [ASL STEM Wiki: Dataset and Benchmark for Interpreting STEM Articles](https://mlsanity.com/paper/2411.05783/) (2024)
- [Probing LLMs for Multilingual Discourse Generalization Through a Unified Label Set](https://mlsanity.com/paper/2503.10515/) (2025)
- [SiLVERScore: Semantically-Aware Embeddings for Sign Language Generation Evaluation](https://mlsanity.com/paper/2509.03791/) (2025)
- [Gloss2Text: Sign Language Gloss translation using LLMs and Semantically Aware Label Smoothing](https://mlsanity.com/paper/2407.01394/) (2024)
- [Generating Signed Language Instructions in Large-Scale Dialogue Systems](https://mlsanity.com/paper/2410.14026/) (2024)
- [Studying and Mitigating Biases in Sign Language Understanding Models](https://mlsanity.com/paper/2410.05206/) (2024)
- [Leveraging Large Language Models for Accurate Sign Language Translation in Low-Resource Scenarios](https://mlsanity.com/paper/2508.18183/) (2025)
- [Bootstrapping Sign Language Annotations with Sign Language Models](https://mlsanity.com/paper/2604.07606/) (2026)

## Abstract (arXiv)

Sign language processing systems have traditionally operated at the sentence level, ignoring critical discourse phenomena fundamental to sign language comprehension. We introduce DiscoSign, a computational approach for discourse-aware text to sign language gloss translation grounded in linguistic research. We address three key phenomena within our modular Large Language Model (LLM)-based translation framework: (i) spatial coreference resolution, where entities maintain consistent spatial locations throughout discourse; (ii) Question-Answer Clauses (QACs), pseudocleft structures serving specific discourse functions; and (iii) concept-gloss consistency, ensuring stable mappings between English concepts and American Sign Language (ASL) signs. Traditional translation metrics fail to capture discourse-level quality, so we introduce a suite of novel evaluation metrics designed to assess each dimension of discourse coherence addressed by our framework. Experiments on sentence-level and discourse-level datasets show that our approach for discourse-aware processing significantly improves spatial consistency and entity tracking relative to sentence-only translation, while maintaining competitive single-sentence gloss translation quality. Our work establishes the first systematic framework for discourse-level text to sign language gloss translation with corresponding evaluation methodology.