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        <title>Challenges in Technical Regulatory Text Variation Detection</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Challenges in Technical Regulatory Text Variation Detection
%A Chikati, Shriya Vaagdevi
%A Larkin, Samuel
%A Minicola, David
%A Lo, Chi-kiu
%Y Gokhan, Tuba
%Y Wang, Kexin
%Y Gurevych, Iryna
%Y Briscoe, Ted
%S Proceedings of the 1st Regulatory NLP Workshop (RegNLP 2025)
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F chikati-etal-2025-challenges
%X 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.
%U https://aclanthology.org/2025.regnlp-1.2/
%P 5-9

Markdown (Informal)

[Challenges in Technical Regulatory Text Variation Detection](https://aclanthology.org/2025.regnlp-1.2/) (Chikati et al., RegNLP 2025)

• Challenges in Technical Regulatory Text Variation Detection (Chikati et al., RegNLP 2025)

ACL

• Shriya Vaagdevi Chikati, Samuel Larkin, David Minicola, and Chi-kiu Lo. 2025. Challenges in Technical Regulatory Text Variation Detection. In Proceedings of the 1st Regulatory NLP Workshop (RegNLP 2025), pages 5–9, Abu Dhabi, UAE. Association for Computational Linguistics.