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Cross-sector generalization of accident-process role classification in occupational accident narratives
arxiv.orgpaper

Cross-sector generalization of accident-process role classification in occupational accident narratives

This paper evaluates cross-sector generalization of a role classifier that structures French occupational accident narratives into four accident-process roles (work situation, unfavourable condition, accident event, consequence). Classifiers trained solely on 42,244 construction-sector factual units are tested without retraining on metallurgy, chemistry-plastics, and an independent company corpus, comparing frozen pretrained representations against task-specific fine-tuning and supervised representation learning. Task-adapted strategies consistently outperformed frozen representations, reaching 85.6-85.8% average balanced accuracy across unseen sectors, supporting transferable assisted-coding systems for occupational risk analysis.

TL;DR Fine-tuned language models generalize across industry sectors for classifying roles in French workplace-accident narratives, hitting ~85.7% balanced accuracy on unseen domains.

Techniques named here

  • Accident-process role classification
  • Cross-sector generalization evaluation

Also named here

  • Transformer