Historically, sign language machine translation has been posed as a
sentence-level task: datasets consisting of continuous narratives are chopped
up and presented to the model as isolated clips. In this work, we explore the
limitations of this task framing. First, we survey a number of linguistic
phenomena in sign languages that depend on discourse-level context. Then as a
case study, we perform the first human baseline for sign language translation
that actually substitutes a human into the machine learning task framing,
rather than provide the human with the entire document as context. This human
baseline -- for ASL to English translation on the How2Sign dataset -- shows
that for 33% of sentences in our sample, our fluent Deaf signer annotators were
only able to understand key parts of the clip in light of additional
discourse-level context. These results underscore the importance of
understanding and sanity checking examples when adapting machine learning to
new domains.

历史上，手语机器翻译一直被认为是一个基于句子级任务的：数据集由连续的叙述切割并作为孤立的片段呈现给模型。在这项工作中，我们探索了这一任务框架的局限性。首先，我们调查了一些手语中依赖于话语级上下文的语言现象。然后，作为一个研究案例，我们进行了手语翻译的第一个人类基准，实际上是将人类置于机器学习任务框架中，而不是将整个文档作为上下文提供给人类。这个人类基准 —— 针对 How2Sign 数据集的 ASL 到英语翻译 —— 显示，在我们的样本中，对于 33% 的句子，我们流利的聋人手语翻译员只能在附加的话语级上下文中理解片段的关键部分。这些结果强调了在将机器学习应用于新领域时理解和检查示例的重要性。