@inproceedings{patil-etal-2019-roll,
title = "Roll Call Vote Prediction with Knowledge Augmented Models",
author = "Patil, Pallavi and
Myer, Kriti and
Zala, Ronak and
Singh, Arpit and
Mysore, Sheshera and
McCallum, Andrew and
Benton, Adrian and
Stent, Amanda",
editor = "Bansal, Mohit and
Villavicencio, Aline",
booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/K19-1053",
doi = "10.18653/v1/K19-1053",
pages = "574--581",
abstract = "The official voting records of United States congresspeople are preserved as roll call votes. Prediction of voting behavior of politicians for whom no voting record exists, such as individuals running for office, is important for forecasting key political decisions. Prior work has relied on past votes cast to predict future votes, and thus fails to predict voting patterns for politicians without voting records. We address this by augmenting a prior state of the art model with multiple sources of external knowledge so as to enable prediction on unseen politicians. The sources of knowledge we use are news text and Freebase, a manually curated knowledge base. We propose augmentations based on unigram features for news text, and a knowledge base embedding method followed by a neural network composition for relations from Freebase. Empirical evaluation of these approaches indicate that the proposed models outperform the prior system for politicians with complete historical voting records by 1.0{\%} point of accuracy (8.7{\%} error reduction) and for politicians without voting records by 33.4{\%} points of accuracy (66.7{\%} error reduction). We also show that the knowledge base augmented approach outperforms the news text augmented approach by 4.2{\%} points of accuracy.",
}
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<abstract>The official voting records of United States congresspeople are preserved as roll call votes. Prediction of voting behavior of politicians for whom no voting record exists, such as individuals running for office, is important for forecasting key political decisions. Prior work has relied on past votes cast to predict future votes, and thus fails to predict voting patterns for politicians without voting records. We address this by augmenting a prior state of the art model with multiple sources of external knowledge so as to enable prediction on unseen politicians. The sources of knowledge we use are news text and Freebase, a manually curated knowledge base. We propose augmentations based on unigram features for news text, and a knowledge base embedding method followed by a neural network composition for relations from Freebase. Empirical evaluation of these approaches indicate that the proposed models outperform the prior system for politicians with complete historical voting records by 1.0% point of accuracy (8.7% error reduction) and for politicians without voting records by 33.4% points of accuracy (66.7% error reduction). We also show that the knowledge base augmented approach outperforms the news text augmented approach by 4.2% points of accuracy.</abstract>
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%0 Conference Proceedings
%T Roll Call Vote Prediction with Knowledge Augmented Models
%A Patil, Pallavi
%A Myer, Kriti
%A Zala, Ronak
%A Singh, Arpit
%A Mysore, Sheshera
%A McCallum, Andrew
%A Benton, Adrian
%A Stent, Amanda
%Y Bansal, Mohit
%Y Villavicencio, Aline
%S Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F patil-etal-2019-roll
%X The official voting records of United States congresspeople are preserved as roll call votes. Prediction of voting behavior of politicians for whom no voting record exists, such as individuals running for office, is important for forecasting key political decisions. Prior work has relied on past votes cast to predict future votes, and thus fails to predict voting patterns for politicians without voting records. We address this by augmenting a prior state of the art model with multiple sources of external knowledge so as to enable prediction on unseen politicians. The sources of knowledge we use are news text and Freebase, a manually curated knowledge base. We propose augmentations based on unigram features for news text, and a knowledge base embedding method followed by a neural network composition for relations from Freebase. Empirical evaluation of these approaches indicate that the proposed models outperform the prior system for politicians with complete historical voting records by 1.0% point of accuracy (8.7% error reduction) and for politicians without voting records by 33.4% points of accuracy (66.7% error reduction). We also show that the knowledge base augmented approach outperforms the news text augmented approach by 4.2% points of accuracy.
%R 10.18653/v1/K19-1053
%U https://aclanthology.org/K19-1053
%U https://doi.org/10.18653/v1/K19-1053
%P 574-581
Markdown (Informal)
[Roll Call Vote Prediction with Knowledge Augmented Models](https://aclanthology.org/K19-1053) (Patil et al., CoNLL 2019)
ACL
- Pallavi Patil, Kriti Myer, Ronak Zala, Arpit Singh, Sheshera Mysore, Andrew McCallum, Adrian Benton, and Amanda Stent. 2019. Roll Call Vote Prediction with Knowledge Augmented Models. In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), pages 574–581, Hong Kong, China. Association for Computational Linguistics.