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

DevFeed: [DiscoSign: Discourse-Aware Text to Sign Language Gloss Translation](<https://devfeed.tech/articles/discosign-discourse-aware-text-to-sign-language-gloss-translation-6728.md>)

Original publisher: [Read original article](<https://machinelearning.apple.com/research/discosign-gloss-translation>)

Published: 2026-09-11T00:00:00Z

Content type: article

Language: en

Sources: [Apple Machine Learning Research](<https://devfeed.tech/sources/apple-machine-learning-research.md>)

Topics: [AI, ML & Data Engineering](<https://devfeed.tech/topics/ai-ml-data-engineering.md>), [Language models](<https://devfeed.tech/topics/language-models.md>)

Tags: [evaluation](<https://devfeed.tech/tags/evaluation.md>), [experiments](<https://devfeed.tech/tags/experiments.md>), [framework](<https://devfeed.tech/tags/framework.md>), [llm](<https://devfeed.tech/tags/llm.md>), [metrics](<https://devfeed.tech/tags/metrics.md>)

## AI overview

DiscoSign is an LLM-based framework for translating text to sign-language gloss while preserving discourse-level coherence. It targets spatial coreference, Question-Answer Clauses, and consistent English-concept-to-ASL-sign mappings, with evaluation metrics for these dimensions.

## Source excerpt

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...