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.

翻译：餐厅的晚餐可能因顾客享受到的意外方面而成为难忘体验，例如等候区设置折纸制作站。如果能预先获知餐厅体验中非典型的方面，便可据此推荐可能带来意外惊喜的体验，进一步提升用户满意度。尽管非典型方面相对罕见，但因其令人难忘的特性，一旦遇到往往会在评论中被提及。据此，本文提出在客户评论中检测非典型方面的任务。为促进提取模型的发展，我们人工标注了三个领域（餐厅、酒店和美发沙龙）的评论基准数据集，并利用这些数据集评估了多个语言模型，包括对基于指令的文本到文本转换器Flan-T5进行微调，以及GPT-3.5的零样本和少样本提示方法。