Multipurpose Intelligent Process Automation via Conversational Assistant

TL;DR

This paper presents a conversational assistant for Intelligent Process Automation that operates in real-world industrial settings, reducing manual tasks and generating training data through user interaction, enhanced by Transfer Learning techniques.

Contribution

It introduces a novel IPA conversational assistant designed for unstructured data environments and demonstrates how user interactions can be leveraged to improve system components using Transfer Learning.

Findings

Reduces repetitive tasks for workers

Enhances training data through user interactions

Improves system components with Transfer Learning

Abstract

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.