Understanding the Language of Political Agreement and Disagreement in Legislative Texts (2020.acl-main)
Copied to clipboard
| Challenge: | Despite the fact that state-level legislation is rarely discussed, it has a dramatic influence on the everyday life of residents of the respective states. |
| Approach: | They propose a large-scale dataset linking state bills and legislator information, geographical information about their districts, and donations and donors’ information. |
| Outcome: | The proposed model improves over strong text-based models by integrating the state-level text and the legislative context. |
Similar Papers
Modeling U.S. State-Level Policies by Extracting Winners and Losers from Legislative Texts (2022.acl-long)
Copied to clipboard
| Challenge: | State-level legislation is the cornerstone of national policies and has long-lasting effects on residents of US states. |
| Approach: | They build a dataset for multiple US states that interconnects multiple sources of data including bills, stakeholders, legislators, and money donors. |
| Outcome: | The proposed model predicts winners/losers of bills and then utilizes them to determine the legislative body’s vote breakdown according to demographic/ideological criteria, e.g., gender. |
Analysis of State-Level Legislative Process in Enhanced Linguistic and Nationwide Network Contexts (2024.naacl-long)
Copied to clipboard
| Challenge: | a new framework for understanding state-level legislative process improves understanding of state legislation and its implications. |
| Approach: | They propose to use generative large language models to decode legislators' behavior and implications of state policies by establishing a shared nationwide network. |
| Outcome: | The framework 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. |
How Predictable is Your State? Leveraging Lexical and Contextual Information for Predicting Legislative Floor Action at the State Level (C18-1)
Copied to clipboard
| Challenge: | a study of state legislative initiatives shows that state legislatures have significant power over certain areas. |
| Approach: | They propose to use lexical content of over 1 million bills to build predictive models . they also use contextual legislature and legislator derived features to compare models based on state specific baselines . |
| Outcome: | The proposed models improve on baselines in all 50 states and D.C. lexical content, contextual features and legislative processes are used to build the models. |
Computational Analysis of Political Texts: Bridging Research Efforts Across Communities (P19-4)
Copied to clipboard
| Challenge: | Political scientists have developed and adopted natural language processing (NLP) methods to exploit text as an additional source of data in their analyses. |
| Approach: | This tutorial aims to provide a gentle introduction to methods and tasks related to computational analysis of political texts from both communities. |
| Outcome: | The main goal of this tutorial is to bring the two research communities closer to each other and contribute to faster and more significant developments in this interdisciplinary area. |
Party Matters: Enhancing Legislative Embeddings with Author Attributes for Vote Prediction (P18-2)
Copied to clipboard
| Challenge: | Existing work on roll-call prediction limited to single session settings, thus not allowing for generalization across sessions. |
| Approach: | They propose a neural method that takes advantage of Congressional voting records to model voting behavior. |
| Outcome: | The proposed method achieves an average of 4% accuracy over the previous state-of-the-art. |
Align Voting Behavior with Public Statements for Legislator Representation Learning (2021.acl-long)
Copied to clipboard
| Challenge: | Existing studies rely on roll call data to estimate political preference of legislators. |
| Approach: | They propose to integrate voting behavior and public statements on Twitter to jointly model legislators. |
| Outcome: | The proposed model improves on the task of roll call vote prediction . it also shows that the model captures nuances in statements . |
Helping a Friend or Supporting a Cause? Disentangling Active and Passive Cosponsorship in the U.S. Congress (2023.acl-long)
Copied to clipboard
| Challenge: | In the U.S. Congress, legislators can use active and passive cosponsorship to support bills. |
| Approach: | They develop an Encoder+RGCN based model that learns legislator representations from bill texts and speech transcripts and uses them to predict voting decisions. |
| Outcome: | The proposed model predicts active and passive cosponsorship with an F1-score of 0.88. |
How to Do Politics with Words: Investigating Speech Acts in Parliamentary Debates (2024.lrec-main)
Copied to clipboard
| Challenge: | a new perspective on framing through the lens of speech acts investigates how politicians make use of different pragmatic speech act functions in political debates. |
| Approach: | They propose a new framework for framing through the lens of speech acts and an annotation scheme for political debates. |
| Outcome: | The proposed framework can predict speech acts with an avg. F1 of around 82.0% . the proposed framework is based on a dataset of German parliamentary debates . |
Language of Bargaining (2023.acl-long)
Copied to clipboard
| Challenge: | a new dataset is being developed to study how language shapes bilateral bargaining . a recent study examined the use of language in negotiation education . |
| Approach: | They propose a dataset to study how language shapes bilateral bargaining . they recruit participants via behavioral labs instead of crowdsourcing platforms . |
| Outcome: | The proposed dataset is based on an exercise in negotiation education . it shows that when subjects can talk, negotiations finish faster and prices drop . |
BillSum: A Corpus for Automatic Summarization of US Legislation (D19-54)
Copied to clipboard
| Challenge: | In the US Congress, over 10,000 bills are introduced each year, with state legislatures introducing tens of thousands of bills. |
| Approach: | They introduce the first dataset for summarizing US Congressional and California state bills . they demonstrate that models built on Congressional bills can be used to summarize California billa . |
| Outcome: | The proposed summarization methods can be applied to states without human-written summaries. |