How does the IPA label data after interacting with users?
It defined a sequence labeling task to extract custom entities from user input and label the next action (out of 13  custom actions defined).
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.
Robotic Process Automation (RPA) is a type of software bots that simulates hand-operated human activities like entering data into a system, registering into accounts, and accomplishing straightforward but repetitive workflows BIBREF0. However, one of the drawbacks of RPA-bots is their susceptibility to changes in defined scenarios: being designed for a particular task, the RPA-bot is usually not adaptable to other domains or even light modifications in a workflow BIBREF0. This inability to readjust to shifting conditions gave rise to Intelligent Process Automation (IPA) systems. IPA-bots combine RPA with Artificial Intelligence (AI) and thus are able to execute more cognitively demanding tasks that require i.a. reasoning and language understanding. Hence, IPA-bots advanced beyond automating shallow “click tasks” and can perform jobs more intelligently – by means of machine learning algorithms. Such IPA-systems undertake time-consuming and routine tasks, and thus enable smart workflows and free up skilled workers to accomplish higher-value activities. One of the potential applications of Natural Language Processing (NLP) within the IPA domain are conversational interfaces that enable human-to-machine interaction. The main benefit of conversational systems is their ability to give attention to several users simultaneously while supporting natural communication. A conventional dialogue system comprises multiple stages and involves different types of NLP subtasks, starting with Natural Language Understanding (NLU) (e.g., intent classification, named entity extraction) and going towards dialogue management (i.e., determining the next possible bot action, considering the dialogue history) and response generation (e.g., converting the semantic representation of the next system action into a natural language utterance). A typical dialogue system for IPA purposes undertakes shallow customer support requests (e.g., answering of FAQs), allowing human workers to focus on more sophisticated inquiries. Recent research in the dialogue generation domain is conducted by employing AI-techniques like machine and deep learning BIBREF1, BIBREF2. However, conventional supervised methods have limitations when applied to real-world data and industrial tasks. The primary challenge here refers to a training phase since a robust model requires an extensive amount of structured and labeled data, that is often not available for domain-specific problems. Especially if it concerns dialogue data, which has to be appropriately structured as well as labeled and annotated with additional information. Therefore, despite the popularity of deep learning end-to-end models, one still needs to rely on conventional pipelines in practical dialogue engineering, especially while setting a new domain. However, with few structured data available, transfer learning methods can be used. Such algorithms enable training of the systems with less or even a minimal amount of data, and are able to transfer the knowledge obtained during the training on existing data to the unseen domain.