We are very excited to join forces with MLCommons and OctoML.ai! Contact Grigori Fursin for more details!

Attention Fusion Networks: Combining Behavior and E-mail Content to Improve Customer Support

lib:3474eac106f9542f (v1.0.0)

Authors: Stephane Fotso,Philip Spanoudes,Benjamin C. Ponedel,Brian Reynoso,Janet Ko
ArXiv: 1811.03169
Document:  PDF  DOI 
Abstract URL: http://arxiv.org/abs/1811.03169v2

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.

Relevant initiatives  

Related knowledge about this paper Reproduced results (crowd-benchmarking and competitions) Artifact and reproducibility checklists Common formats for research projects and shared artifacts Reproducibility initiatives


Please log in to add your comments!
If you notice any inapropriate content that should not be here, please report us as soon as possible and we will try to remove it within 48 hours!