Extraction of Atypical Aspects from Customer Reviews: Datasets and Experiments with Language Models

• URL: http://arxiv.org/abs/2311.02702v1
• Date: Sun, 5 Nov 2023 16:15:50 GMT
• Title: Extraction of Atypical Aspects from Customer Reviews: Datasets and Experiments with Language Models
• Authors: Smita Nannaware and Erfan Al-Hossami and Razvan Bunescu
• Abstract summary: 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. We evaluate a number of language models, ranging from fine-tuning the instruction-based text-to-text transformer-T5 to zero-shot and few-shot prompting of GPT-3.5.
• Score: 0.7234862895932991
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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