Intelligent Process Automation (IPA) is an emerging technology with a primary goal to assist the knowledge worker by taking care of repetitive, routine and low-cognitive tasks. Conversational agents that can interact with users in a natural language are potential application for IPA systems. Such intelligent agents can assist the user by answering specific questions and executing routine tasks that are ordinarily performed in a natural language (i.e., customer support). In this work, we tackle a challenge of implementing an IPA conversational assistant in a real-world industrial setting with a lack of structured training data. Our proposed system brings two significant benefits: First, it reduces repetitive and time-consuming activities and, therefore, allows workers to focus on more intelligent processes. Second, by interacting with users, it augments the resources with structured and to some extent labeled training data. We showcase the usage of the latter by re-implementing several components of our system with Transfer Learning (TL) methods.
@article{arxiv.2001.02284,
title = {Multipurpose Intelligent Process Automation via Conversational Assistant},
author = {Alena Moiseeva and Dietrich Trautmann and Michael Heimann and Hinrich Schütze},
journal= {arXiv preprint arXiv:2001.02284},
year = {2020}
}
Comments
Presented at the AAAI-20 Workshop on Intelligent Process Automation