Multi-environment Topic Models
Keywords: Topic Models, ML for Social Science, Out-of-distribution Generalization, Multi-environment learning
TL;DR: We propose multi-environment topic models, unsupervised probabilistic text models designed to uncover consistent topics across varying sources.
Abstract: Probabilistic topic models are a powerful tool for extracting latent themes from large text datasets. In many text datasets, we also observe per-document covariates (e.g., source, style, political affiliation) that act as environments that modulate a "global" (environment-agnostic) topic representation. Accurately learning these representations is important for prediction on new documents in unseen environments and for estimating the causal effect of topics on real-world outcomes. To this end, we introduce the Multi-environment Topic Model (MTM), an unsupervised probabilistic model that separates global and environment-specific terms. Through experimentation on various political content, from ads to tweets and speeches, we show that the MTM produces interpretable global topics with distinct environment-specific words. On multi-environment data, the MTM outperforms strong baselines in and out-of-distribution. It also enables the discovery of accurate causal effects.
Supplementary Material: zip
Primary Area: interpretability and explainable AI
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Submission Number: 7691
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