A Simple End-to-End Question Answering Model for Product Information
Tuan Lai, Trung Bui, Sheng Li, Nedim Lipka · 2018
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.