Anastassia Kornilova


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An Item Response Theory Framework for Persuasion
Anastassia Kornilova | Vladimir Eidelman | Daniel Douglass
Findings of the Association for Computational Linguistics: NAACL 2022

In this paper, we apply Item Response Theory, popular in education and political science research, to the analysis of argument persuasiveness in language. We empirically evaluate the model’s performance on three datasets, including a novel dataset in the area of political advocacy. We show the advantages of separating these components under several style and content representations, including evaluating the ability of the speaker embeddings generated by the model to parallel real-world observations about persuadability.


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BillSum: A Corpus for Automatic Summarization of US Legislation
Anastassia Kornilova | Vladimir Eidelman
Proceedings of the 2nd Workshop on New Frontiers in Summarization

Automatic summarization methods have been studied on a variety of domains, including news and scientific articles. Yet, legislation has not previously been considered for this task, despite US Congress and state governments releasing tens of thousands of bills every year. In this paper, we introduce BillSum, the first dataset for summarization of US Congressional and California state bills. We explain the properties of the dataset that make it more challenging to process than other domains. Then, we benchmark extractive methods that consider neural sentence representations and traditional contextual features. Finally, we demonstrate that models built on Congressional bills can be used to summarize California billa, thus, showing that methods developed on this dataset can transfer to states without human-written summaries.


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Party Matters: Enhancing Legislative Embeddings with Author Attributes for Vote Prediction
Anastassia Kornilova | Daniel Argyle | Vladimir Eidelman
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Predicting how Congressional legislators will vote is important for understanding their past and future behavior. However, previous work on roll-call prediction has been limited to single session settings, thus not allowing for generalization across sessions. In this paper, we show that text alone is insufficient for modeling voting outcomes in new contexts, as session changes lead to changes in the underlying data generation process. We propose a novel neural method for encoding documents alongside additional metadata, achieving an average of a 4% boost in accuracy over the previous state-of-the-art.

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How Predictable is Your State? Leveraging Lexical and Contextual Information for Predicting Legislative Floor Action at the State Level
Vladimir Eidelman | Anastassia Kornilova | Daniel Argyle
Proceedings of the 27th International Conference on Computational Linguistics

Modeling U.S. Congressional legislation and roll-call votes has received significant attention in previous literature, and while legislators across 50 state governments and D.C. propose over 100,000 bills each year, enacting over 30% of them on average, state level analysis has received relatively less attention due in part to the difficulty in obtaining the necessary data. Since each state legislature is guided by their own procedures, politics and issues, however, it is difficult to qualitatively asses the factors that affect the likelihood of a legislative initiative succeeding. We present several methods for modeling the likelihood of a bill receiving floor action across all 50 states and D.C. We utilize the lexical content of over 1 million bills, along with contextual legislature and legislator derived features to build our predictive models, allowing a comparison of what factors are important to the lawmaking process. Furthermore, we show that these signals hold complementary predictive power, together achieving an average improvement in accuracy of 18% over state specific baselines.