Extraction of Atypical Aspects from Customer Reviews: Datasets and Experiments with Language Models
This work addresses the need for serendipitous recommendations in domains like restaurants and hotels, though it is incremental as it builds on existing language models for a new task.
The paper tackles the problem of detecting atypical aspects in customer reviews to enhance recommendations, by introducing a new task and manually annotating benchmark datasets across three domains, and evaluates language models like Flan-T5 and GPT-3.5, achieving competitive performance with specific gains in extraction accuracy.
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.