Analysis of State-Level Legislative Process in Enhanced Linguistic and Nationwide Network Contexts
Maryam Davoodi, Dan Goldwasser
Abstract
State bills have a significant impact on various aspects of society, including health, education, and the economy. Consequently, it is crucial to conduct systematic research on state bills before and after they are enacted to evaluate their benefits and drawbacks, thereby guiding future decision-making. In this work, we developed the first state-level deep learning framework that (1) handles the complex and inconsistent language of policies across US states using generative large language models and (2) decodes legislators’ behavior and implications of state policies by establishing a shared nationwide network, enriched with diverse contexts, such as information on interest groups influencing public policy and legislators’ courage test results, which reflect their political positions.
BibTeX
@inproceedings{davoodi-goldwasser-2024-analysis,
title = "Analysis of State-Level Legislative Process in Enhanced Linguistic and Nationwide Network Contexts",
author = "Davoodi, Maryam and
Goldwasser, Dan",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-long.411/",
doi = "10.18653/v1/2024.naacl-long.411",
pages = "7404--7422"
}