Papers with legislator representations
Helping a Friend or Supporting a Cause? Disentangling Active and Passive Cosponsorship in the U.S. Congress (2023.acl-long)
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| 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. |
PAR: Political Actor Representation Learning with Social Context and Expert Knowledge (2022.emnlp-main)
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Shangbin Feng, Zhaoxuan Tan, Zilong Chen, Ningnan Wang, Peisheng Yu, Qinghua Zheng, Xiaojun Chang, Minnan Luo
| Challenge: | Existing approaches focus on textual data and voting records to induce political actors' stances. |
| Approach: | They propose a Political Actor Representation learning framework that leverages social context and expert knowledge to model ideological stances. |
| Outcome: | The proposed framework improves political text understanding and improves roll call vote prediction and political perspective detection. |