A transformers-based approach on industrial disaster consequence identification from accident narratives

Abstract

Industrial accidents are a major part of the more general technological disasters domain. These events vary from mild to severe and can affect millions of people and cause huge environmental and property damage. New technologies enable novel approaches and applications in the field of disaster management which help prevent, mitigate, and respond to such events. More specifically, transformers-based language models dominate natural language applications with outstanding state of the art performance. In this study an effort is being made to identify and evaluate the ability of transformers to extract and evaluate the consequences and effects of an industrial accident using natural language. The eMARS database is used for training data and a BERT model is used for fine tuning. The results on a multilabel classification task for consequence prediction from accident narratives are promising, achieving an overall weighted F1-score of 0.80.