AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。
content type paperpublished September 2026 DiscoSign: Discourse-Aware Text to Sign Language Gloss Translation AuthorsVasileios Baltatzis‡, Mert Inan‡†, Connor Gillis, Raja Kushalnagar§, Lorna Quandt§, Leah Findlater, Colin Lea View publication 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. ‡ Equal contribution † Northeastern University § Gallaudet University Work done while at Apple Bootstrapping Sign Language Annotations with Sign Language Models April 30, 2026research area Accessibility, research area Computer Visionconference CVPR AI-driven sign language interpretation is limited by a lack of high-quality annotated data. New datasets including ASL STEM Wiki and FLEURS-ASL contain professional interpreters and 100s of hours of data but remain only partially annotated and thus underutilized, in part due to the prohibitive costs of annotating at this scale. In this work, we develop a pseudo-annotation pipeline that takes signed video and English as input and outputs a ranked… Read more Towards AI-Driven Sign Language Generation with Non-Manual Markers March 7, 2025research area Accessibility, research area Human-Computer Interactionconference CHI Sign languages are essential for the Deaf and Hard-of-Hearing (DHH) community. Sign language generation systems have the potential to support communication by translating from written languages, such as English, into signed videos. However, current systems often fail to meet user needs due to poor translation of grammatical structures, the absence of facial cues and body language, and insufficient visual and motion fidelity. We address these… Read more