Name
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. |
Year | Venue | Field |
|---|---|---|
2018 | IWSDS | Question answering,Ranking,Computer science,Question answer,Recurrent neural network,Artificial intelligence,Natural language processing,Product description,Sentence,Encoding (memory) |
DocType | Volume | Citations |
Journal | abs/1802.01766 | 1 |
PageRank | References | Authors |
0.35 | 6 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Girish Kumar | 1 | 9 | 3.03 |
| Matthew Henderson | 2 | 158 | 8.90 |
| Shannon Chan | 3 | 1 | 0.35 |
| Hoang Nguyen | 4 | 42 | 7.49 |
| Lucas Ngoo | 5 | 1 | 0.35 |