While national politics often receive the spotlight, the overwhelming majority of legislation proposed, discussed, and enacted is done at the state level. Despite this fact, there is little awareness of the dynamics that lead to adopting these policies. In this paper, we take the first step towards a better understanding of these processes and the underlying dynamics that shape them, using data-driven methods. We build a new large-scale dataset, from multiple data sources, connecting state bills and legislator information, geographical information about their districts, and donations and donors’ information. We suggest a novel task, predicting the legislative body’s vote breakdown for a given bill, according to different criteria of interest, such as gender, rural-urban and ideological splits. Finally, we suggest a shared relational embedding model, representing the interactions between the text of the bill and the legislative context in which it is presented. Our experiments show that providing this context helps improve the prediction over strong text-based models.

In this work, we study collaborative online conversations. Such conversations are rich in content, constructive and motivated by a shared goal. Automatically identifying such conversations requires modeling complex discourse behaviors, which characterize the flow of information, sentiment and community structure within discussions. To help capture these behaviors, we define a hybrid relational model in which relevant discourse behaviors are formulated as discrete latent variables and scored using neural networks. These variables provide the information needed for predicting the overall collaborative characterization of the entire conversational thread. We show that adding inductive bias in the form of latent variables results in performance improvement, while providing a natural way to explain the decision.

We propose an end-to-end variational autoencoding parsing (VAP) model for semi-supervised graph-based projective dependency parsing. It encodes the input using continuous latent variables in a sequential manner by deep neural networks (DNN) that can utilize the contextual information, and reconstruct the input using a generative model. The VAP model admits a unified structure with different loss functions for labeled and unlabeled data with shared parameters. We conducted experiments on the WSJ data sets, showing the proposed model can use the unlabeled data to increase the performance on a limited amount of labeled data, on a par with a recently proposed semi-supervised parser with faster inference.