@inproceedings{ramrakhiyani-etal-2021-extracting,
    title = "Extracting Events from Industrial Incident Reports",
    author = "Ramrakhiyani, Nitin  and
      Hingmire, Swapnil  and
      Patil, Sangameshwar  and
      Kumar, Alok  and
      Palshikar, Girish",
    editor = {H{\"u}rriyeto{\u{g}}lu, Ali},
    booktitle = "Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Author-page-Marten-During-lu/2021.case-1.9/",
    doi = "10.18653/v1/2021.case-1.9",
    pages = "58--67",
    abstract = "Incidents in industries have huge social and political impact and minimizing the consequent damage has been a high priority. However, automated analysis of repositories of incident reports has remained a challenge. In this paper, we focus on automatically extracting events from incident reports. Due to absence of event annotated datasets for industrial incidents we employ a transfer learning based approach which is shown to outperform several baselines. We further provide detailed analysis regarding effect of increase in pre-training data and provide explainability of why pre-training improves the performance."
}