Question-Answer Selection in User to User Marketplace Conversations
Girish Kumar, Matthew Henderson, Shannon Chan, Hoang Nguyen, Lucas, Ngoo

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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
