Question-Answer Selection in User to User Marketplace Conversations
Computation and Language
2018-02-07 v1
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 590K 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.
Keywords
Cite
@article{arxiv.1802.01766,
title = {Question-Answer Selection in User to User Marketplace Conversations},
author = {Girish Kumar and Matthew Henderson and Shannon Chan and Hoang Nguyen and Lucas Ngoo},
journal= {arXiv preprint arXiv:1802.01766},
year = {2018}
}