Sellers in user to user marketplaces can be inundated with questions from potential buyers. Answers are often already available in the product description. We collected a dataset of around 590 K such questions and answers from conversations in an online marketplace. We propose a question answering system that selects a sentence from the product description using a neural-network ranking model. We explore multiple encoding strategies, with recurrent neural networks and feed-forward attention layers yielding good results. This paper presents a demo to interactively pose buyer questions and visualize the ranking scores of product description sentences from live online listings.
CITATION STYLE
Kumar, G., Henderson, M., Chan, S., Nguyen, H., & Ngoo, L. (2019). Question-answer selection in user to user marketplace conversations. In Lecture Notes in Electrical Engineering (Vol. 579, pp. 397–403). Springer. https://doi.org/10.1007/978-981-13-9443-0_35
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