@inproceedings{akarajaradwong-etal-2026-public,
title = "What the Public Asked to Change, and What It Would Touch? Complaint-to-Statute Grounding for {T}hailand{'}s Regulatory Reform",
author = "Akarajaradwong, Pawitsapak and
Lertprasertphakorn, Wuttikrai and
Chaksangchaichot, Chompakorn and
Nutanong, Sarana",
editor = "Aletras, Nikolaos and
Chalkidis, Ilias and
Barrett, Leslie and
Goanț{\u{a}}, C{\u{a}}t{\u{a}}lina and
Preoțiuc-Pietro, Daniel and
Spanakis, Gerasimos and
Xu, Shanshan",
booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.nllp-1.13/",
pages = "133--141",
ISBN = "979-8-89176-494-1",
abstract = "When a government reviews its laws, the public speaks in everyday words and rarely names a provision directly, so turning complaints into amendment targets is still expert work done by hand. We describe a pipeline deployed inside Thailand{'}s 2026 Regulatory Guillotine that maps 199,771 social media posts and 63 proposals from business associations to the related specific provisions. Our pipeline first removes irrelevant posts. A legal language model then labels each post with a written rationale, and the posts' embeddings are clustered into 309 issues. The model grounds each issue in specific provisions using a Thai legal knowledge graph, which it must read before it cites, leaving 799 verified sections in 80 laws. The same graph then shows what an amendment would touch, namely the other sections, definitions, and case law that depend on each targeted section, a check that drafters currently do by hand. We report the system, what the Thai public asked to change, and the blast radius of the sections they targeted."
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<abstract>When a government reviews its laws, the public speaks in everyday words and rarely names a provision directly, so turning complaints into amendment targets is still expert work done by hand. We describe a pipeline deployed inside Thailand’s 2026 Regulatory Guillotine that maps 199,771 social media posts and 63 proposals from business associations to the related specific provisions. Our pipeline first removes irrelevant posts. A legal language model then labels each post with a written rationale, and the posts’ embeddings are clustered into 309 issues. The model grounds each issue in specific provisions using a Thai legal knowledge graph, which it must read before it cites, leaving 799 verified sections in 80 laws. The same graph then shows what an amendment would touch, namely the other sections, definitions, and case law that depend on each targeted section, a check that drafters currently do by hand. We report the system, what the Thai public asked to change, and the blast radius of the sections they targeted.</abstract>
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%0 Conference Proceedings
%T What the Public Asked to Change, and What It Would Touch? Complaint-to-Statute Grounding for Thailand’s Regulatory Reform
%A Akarajaradwong, Pawitsapak
%A Lertprasertphakorn, Wuttikrai
%A Chaksangchaichot, Chompakorn
%A Nutanong, Sarana
%Y Aletras, Nikolaos
%Y Chalkidis, Ilias
%Y Barrett, Leslie
%Y Goanță, Cătălina
%Y Preoțiuc-Pietro, Daniel
%Y Spanakis, Gerasimos
%Y Xu, Shanshan
%S Proceedings of the Natural Legal Language Processing Workshop 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-494-1
%F akarajaradwong-etal-2026-public
%X When a government reviews its laws, the public speaks in everyday words and rarely names a provision directly, so turning complaints into amendment targets is still expert work done by hand. We describe a pipeline deployed inside Thailand’s 2026 Regulatory Guillotine that maps 199,771 social media posts and 63 proposals from business associations to the related specific provisions. Our pipeline first removes irrelevant posts. A legal language model then labels each post with a written rationale, and the posts’ embeddings are clustered into 309 issues. The model grounds each issue in specific provisions using a Thai legal knowledge graph, which it must read before it cites, leaving 799 verified sections in 80 laws. The same graph then shows what an amendment would touch, namely the other sections, definitions, and case law that depend on each targeted section, a check that drafters currently do by hand. We report the system, what the Thai public asked to change, and the blast radius of the sections they targeted.
%U https://aclanthology.org/2026.nllp-1.13/
%P 133-141
Markdown (Informal)
[What the Public Asked to Change, and What It Would Touch? Complaint-to-Statute Grounding for Thailand’s Regulatory Reform](https://aclanthology.org/2026.nllp-1.13/) (Akarajaradwong et al., NLLP 2026)
ACL