  Natural language is among the most accessible tools for explaining decisions
to humans, and large pretrained language models (PLMs) have demonstrated
impressive abilities to generate coherent natural language explanations (NLE).
The existing NLE research perspectives do not take the audience into account.
An NLE can have high textual quality, but it might not accommodate audiences'
needs and preference. To address this limitation, we propose an alternative
perspective, \textit{situated} NLE. On the evaluation side, we set up automated
evaluation scores. These scores describe the properties of NLEs in lexical,
semantic, and pragmatic categories. On the generation side, we identify three
prompt engineering techniques and assess their applicability on the situations.
Situated NLE provides a perspective and facilitates further research on the
generation and evaluation of explanations.
