DiscoSign introduces discourse-aware translation from text to ASL gloss using LLMs
DiscoSign is a new framework that extends text‑to‑sign‑language gloss translation beyond the sentence level by incorporating discourse‑level cues. Built on a modular large language model pipeline, it tackles three linguistic challenges: spatial coreference (keeping entity locations consistent across sentences), Question‑Answer Clauses that serve specific discourse functions, and concept‑gloss…
Key points
- DiscoSign adds spatial coreference, QAC handling, and concept‑gloss consistency to sign‑language gloss translation
- Experiments show improved entity tracking and spatial consistency over sentence‑only baselines while keeping single‑sentence quality
- New evaluation metrics assess discourse coherence, filling a gap in sign‑language translation assessment
The authors evaluate DiscoSign on both sentence‑level and discourse‑level datasets, reporting marked gains in spatial consistency and entity tracking compared with traditional sentence‑only systems, while preserving competitive single‑sentence gloss quality. To measure these improvements, they also introduce a suite of novel metrics that capture discourse coherence, addressing a long‑standing gap in sign‑language translation evaluation.
By providing the first systematic approach and evaluation suite for discourse‑aware sign‑language gloss translation, DiscoSign paves the way for more natural, context‑sensitive AI‑driven interpretation tools for the Deaf and Hard‑of‑Hearing community.
DiscoSign: Discourse-Aware Text to Sign Language Gloss Translation
Apple Machine Learning Research · 11 September 2026
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