Multipurpose Intelligent Process Automation via Conversational Assistant
This work addresses the problem of automating routine tasks for knowledge workers in industrial settings, but it appears incremental as it builds on existing conversational agent and transfer learning methods.
The paper tackled the challenge of implementing an Intelligent Process Automation conversational assistant in a real-world industrial setting with limited structured training data, resulting in a system that reduces repetitive tasks and generates labeled data for transfer learning.
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.