手语机器翻译
Machine Translation for Sign Languages
- University of Surrey（萨里大学）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
综述手语机器翻译进展，涵盖从识别到端到端翻译的系统演变、技术挑战及数据治理等伦理考量，强调跨学科合作与聋人社区参与。
中文摘要 AI 辅助
在过去十年中，手语机器翻译取得了显著进展，从孤立手语识别演变为端到端翻译系统。姿态估计、Transformer架构和大规模数据集收集方面的进步推动了这一进展，然而挑战依然存在。与口语资源相比，数据集仍然有限；评估指标未能充分捕捉输出的语言质量；并且模型必须捕捉手语同时性、多层次和三维的结构。本手稿提供了一篇全面的综述，旨在平衡技术挑战与利益相关者考量。我们考察了使手语在计算上具有独特性的语言学特性，追溯了识别、翻译和生成系统的演变，并分析了持续存在的技术挑战。至关重要的是，我们讨论了围绕数据治理、社区参与和适当使用的伦理考量。借鉴涵盖计算机视觉、手语语言学和聋人研究的跨学科视角，我们的分析强调，持续进展需要这些领域之间以及与聋人社区的持续合作。
英文摘要
Sign language machine translation has progressed substantially over the past decade, evolving from isolated sign recognition to end-to-end translation systems. Advances in pose estimation, transformer architectures, and large-scale dataset collection have driven progress, yet challenges remain. Datasets are limited compared to spoken-language resources; evaluation metrics inadequately capture the linguistic quality of output; and models must capture the simultaneous, multi-layered, and three-dimensional structure of sign languages. This manuscript provides a comprehensive review that seeks to balance technical challenges with stakeholder considerations. We examine the linguistic properties that make sign languages computationally unique, trace the evolution of recognition, translation, and production systems, and analyze ongoing technical challenges. Crucially, we address ethical considerations around data governance, community involvement, and appropriate use. Drawing on interdisciplinary perspectives spanning computer vision, sign language linguistics, and deaf studies, our analysis emphasizes that continued progress requires sustained collaboration across these fields and with deaf communities.
