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. |
Similar Papers
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. |
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. |
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. |
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 . |
Toward A Digital Twin of U.S. Congress (2026.findings-acl)
Copied to clipboard
| Challenge: | a virtual model of congresspersons based on a collection of language models meets the definition of a digital twin. |
| Approach: | They propose to use a daily-updated dataset to generate tweets from congresspersons . they show that a modern language model equipped with subsets of this dataset produces Tweets that are indistinguishable from actual Tweets posted by their physical counterparts. |
| Outcome: | The proposed model produces Tweets that are indistinguishable from actual tweets posted by congresspersons. |
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. |
An Embedding Model for Estimating Legislative Preferences from the Frequency and Sentiment of Tweets (2020.emnlp-main)
Copied to clipboard
| Challenge: | Legislator preferences are typically estimated as general ideology using roll call votes on legislation, but these measures fail to capture aspects of preferences not reflected in legislation, such as attitudes towards a sitting president. |
| Approach: | They propose an embedding-based method for measuring legislator attitudes using tweets . they model legislators' attitudes towards president Donald Trump as vector embeddables that interact with embeddibles for Trump himself constructed using a neural network from the text of his daily tweets. |
| Outcome: | The proposed model predicts the frequency and sentiment of tweets by comparing it to traditional measures of legislator preferences. |
Representing Social Media Users for Sarcasm Detection (D18-1)
Copied to clipboard
| Challenge: | Existing annotated corpus of Reddit comments is limited by available annotation methods. |
| Approach: | They propose a Bayesian approach that directly represents authors’ propensities to be sarcastic and a dense embedding approach that can learn interactions between the author and the text. |
| Outcome: | The proposed approach performs better in homogeneous contexts, whereas the dense embeddings prove valuable in more diverse contexts. |
DisSent: Learning Sentence Representations from Explicit Discourse Relations (P19-1)
Copied to clipboard
| Challenge: | Existing models train on vast amounts of text or require costly, manually curated datasets. |
| Approach: | They propose to leverage the discourse relations between sentences to curate a high quality sentence relation task by leveraging explicit discourse relations. |
| Outcome: | The proposed model can be used to learn the meaning of two sentences in a bidirectional LSTM sentence encoder. |