SOTAVerified

Prediction Focused Topic Models via Feature Selection

2019-10-12Code Available0· sign in to hype

Jason Ren, Russell Kunes, Finale Doshi-Velez

Code Available — Be the first to reproduce this paper.

Reproduce

Code

Abstract

Supervised topic models are often sought to balance prediction quality and interpretability. However, when models are (inevitably) misspecified, standard approaches rarely deliver on both. We introduce a novel approach, the prediction-focused topic model, that uses the supervisory signal to retain only vocabulary terms that improve, or at least do not hinder, prediction performance. By removing terms with irrelevant signal, the topic model is able to learn task-relevant, coherent topics. We demonstrate on several data sets that compared to existing approaches, prediction-focused topic models learn much more coherent topics while maintaining competitive predictions.

Tasks

Reproductions