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.