  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.
