A growing effort in NLP aims to build datasets of human explanations. However, the term explanation encompasses a broad range of notions, each with different properties and ramifications. Our goal is to provide an overview of diverse types of explanations and human limitations, and discuss implications for collecting and using explanations in NLP. Inspired by prior work in psychology and cognitive sciences, we group existing human explanations in NLP into three categories: proximal mechanism, evidence, and procedure. These three types differ in nature and have implications for the resultant explanations. For instance, procedure is not considered explanations in psychology and connects with a rich body of work on learning from instructions. The diversity of explanations is further evidenced by proxy questions that are needed for annotators to interpret and answer open-ended why questions. Finally, explanations may require different, often deeper, understandings than predictions, which casts doubt on whether humans can provide useful explanations in some tasks.
翻译:国家实验室方案日益努力建立关于人类解释的数据集,然而,该术语的解释包含广泛的各种概念,每个概念具有不同的特性和后果。我们的目标是提供对各种解释和人的限制的概览,并讨论在国家实验室方案内收集和使用解释的影响。受心理学和认知科学先前工作的启发,我们将国家实验室的现有人类解释分为三类:精密机制、证据和程序。这三类在性质上有所不同,对由此产生的解释有影响。例如,程序不被视为心理学的解释,与从指令中学习的丰富工作相联系。解释的多样性还表现在代用问题上,而代用问题是说明者解释和回答不限定的问题。最后,解释可能要求不同的、往往更深入的理解,而不是预测,这使人们怀疑人类是否能够在某些任务中提供有用的解释。