Question-Answer Selection in User to User Marketplace Conversations

TL;DR

This paper introduces a neural network-based system that selects relevant sentences from product descriptions to answer buyer questions in online marketplaces, improving efficiency and user interaction.

Contribution

It presents a new dataset of marketplace questions and answers and explores neural encoding strategies for sentence selection in this context.

Findings

Recurrent neural networks and attention layers perform well for sentence ranking

The system effectively matches buyer questions with relevant product description sentences

Demo showcases real-time question answering and visualization from live listings

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.