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, situated NLE, including a situated generation framework and a
situated evaluation framework. On the generation side, we propose simple prompt
engineering methods that adapt the NLEs to situations. In human studies, the
annotators preferred the situated NLEs. On the evaluation side, we set up
automated evaluation scores in lexical, semantic, and pragmatic categories. The
scores can be used to select the most suitable prompts to generate NLEs.
Situated NLE provides a perspective to conduct further research on automatic
NLE generations.

自然语言解释、预训练语言模型、情境感知的自然语言解释、提示工程方法、自动化评估分数是本论文研究的关键词和主题。