Leveraging Data Augmentation for Process Information Extraction
Julian Neuberger Leonie Doll Benedict Engelmann Lars Ackermann Stefan Jablonski
Business Process Modeling projects often require formal process models as a
central component. High costs associated with the creation of such formal
process models motivated many different fields of research aimed at automated
generation of process models from readily available data. These include process
mining on event logs, and generating business process models from natural
language texts. Research in the latter field is regularly faced with the
problem of limited data availability, hindering both evaluation and development
of new techniques, especially learning-based ones.
  To overcome this data scarcity issue, in this paper we investigate the
application of data augmentation for natural language text data. Data
augmentation methods are well established in machine learning for creating new,
synthetic data without human assistance. We find that many of these methods are
applicable to the task of business process information extraction, improving
the accuracy of extraction. Our study shows, that data augmentation is an
important component in enabling machine learning methods for the task of
business process model generation from natural language text, where currently
mostly rule-based systems are still state of the art. Simple data augmentation
techniques improved the $F_1$ score of mention extraction by 2.9 percentage
points, and the $F_1$ of relation extraction by $4.5$. To better understand how
data augmentation alters human annotated texts, we analyze the resulting text,
visualizing and discussing the properties of augmented textual data.
  We make all code and experiments results publicly available.