Abstract
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.
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Kumar, G., Henderson, M., Chan, S., Nguyen, H., Ngoo, L. (2019). Question-Answer Selection in User to User Marketplace Conversations. In: D'Haro, L., Banchs, R., Li, H. (eds) 9th International Workshop on Spoken Dialogue System Technology. Lecture Notes in Electrical Engineering, vol 579. Springer, Singapore. https://doi.org/10.1007/978-981-13-9443-0_35
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DOI: https://doi.org/10.1007/978-981-13-9443-0_35
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