Attention Fusion Networks: Combining Behavior and E-mail Content to Improve Customer Support
Computation and Language
2018-11-14 v2 Machine Learning
Abstract
Customer support is a central objective at Square as it helps us build and maintain great relationships with our sellers. In order to provide the best experience, we strive to deliver the most accurate and quasi-instantaneous responses to questions regarding our products. In this work, we introduce the Attention Fusion Network model which combines signals extracted from seller interactions on the Square product ecosystem, along with submitted email questions, to predict the most relevant solution to a seller's inquiry. We show that the innovative combination of two very different data sources that are rarely used together, using state-of-the-art deep learning systems outperforms, candidate models that are trained only on a single source.
Keywords
Cite
@article{arxiv.1811.03169,
title = {Attention Fusion Networks: Combining Behavior and E-mail Content to Improve Customer Support},
author = {Stephane Fotso and Philip Spanoudes and Benjamin C. Ponedel and Brian Reynoso and Janet Ko},
journal= {arXiv preprint arXiv:1811.03169},
year = {2018}
}