| Téléchargement | - Voir la version finale : Challenges in technical regulatory text variation detection (PDF, 787 Kio)
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| Lien | https://aclanthology.org/2025.regnlp-1.2/ |
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| Auteur | Rechercher : Chikati, Shriya Vaagdevi1; Rechercher : Larkin, Samuel1Identifiant ORCID : https://orcid.org/0009-0000-6147-9631; Rechercher : Minicola, David2; Rechercher : Lo, Chi-kiu1Identifiant ORCID : https://orcid.org/0000-0001-8714-7846 |
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| Affiliation | - Conseil national de recherches Canada. Technologies numériques
- Conseil national de recherches Canada. Construction
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| Format | Texte, Article |
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| Conférence | 1st Regulatory NLP Workshop, RegNLP 2025, Janurary 19 - 24, 2025, Abu Dhabi, United Arab Emirates |
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| Résumé | 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. |
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| Date de publication | 2025-01-20 |
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| Maison d’édition | Association for Computational Linguistics |
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| Licence | |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 972b03b1-81fd-4c9e-b3e8-88e308eff012 |
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| Enregistrement créé | 2025-03-12 |
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| Enregistrement modifié | 2025-03-18 |
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