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

Tuan Lai, Trung Bui, Sheng Li, Nedim Lipka

Abstract
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.
Anthology ID:
W18-3105
Volume:
Proceedings of the First Workshop on Economics and Natural Language Processing
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
38–43
Language:
URL:
https://aclanthology.org/W18-3105
DOI:
10.18653/v1/W18-3105
Bibkey:
Cite (ACL):
Tuan Lai, Trung Bui, Sheng Li, and Nedim Lipka. 2018. A Simple End-to-End Question Answering Model for Product Information. In Proceedings of the First Workshop on Economics and Natural Language Processing, pages 38–43, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
A Simple End-to-End Question Answering Model for Product Information (Lai et al., ACL 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/nodalida-main-page/W18-3105.pdf
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