  A restaurant dinner may become a memorable experience due to an unexpected
aspect enjoyed by the customer, such as an origami-making station in the
waiting area. If aspects that are atypical for a restaurant experience were
known in advance, they could be leveraged to make recommendations that have the
potential to engender serendipitous experiences, further increasing user
satisfaction. Although relatively rare, whenever encountered, atypical aspects
often end up being mentioned in reviews due to their memorable quality.
Correspondingly, in this paper we introduce the task of detecting atypical
aspects in customer reviews. To facilitate the development of extraction
models, we manually annotate benchmark datasets of reviews in three domains -
restaurants, hotels, and hair salons, which we use to evaluate a number of
language models, ranging from fine-tuning the instruction-based text-to-text
transformer Flan-T5 to zero-shot and few-shot prompting of GPT-3.5.
