@inproceedings{chikati-etal-2025-challenges,
    title = "Challenges in Technical Regulatory Text Variation Detection",
    author = "Chikati, Shriya Vaagdevi  and
      Larkin, Samuel  and
      Minicola, David  and
      Lo, Chi-kiu",
    editor = "Gokhan, Tuba  and
      Wang, Kexin  and
      Gurevych, Iryna  and
      Briscoe, Ted",
    booktitle = "Proceedings of the 1st Regulatory NLP Workshop (RegNLP 2025)",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.regnlp-1.2/",
    pages = "5--9",
    abstract = "We present a preliminary study on the feasibility of using current natural language processing techniques to detect variations between the construction codes of different jurisdictions. We formulate the task as a sentence alignment problem and evaluate various sentence representation models for their performance in this task. Our results show that task-specific trained embeddings perform marginally better than other models, but the overall accuracy remains a challenge. We also show that domain-specific fine-tuning hurts the task performance. The results highlight the challenges of developing NLP applications for technical regulatory texts."
}