A Simple End-to-End Question Answering Model for Product Information

WS 2018  ·  Tuan Lai, Trung Bui, Sheng Li, Nedim Lipka ·

When evaluating a potential product purchase, customers may have many questions in mind. They want to get adequate information to determine whether the product of interest is worth their money. In this paper we present a simple deep learning model for answering questions regarding product facts and specifications. Given a question and a product specification, the model outputs a score indicating their relevance. To train and evaluate our proposed model, we collected a dataset of 7,119 questions that are related to 153 different products. Experimental results demonstrate that {--}despite its simplicity{--} the performance of our model is shown to be comparable to a more complex state-of-the-art baseline.

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