We present a novel semantic framework for modeling linguistic expressions of generalization---generic, habitual, and episodic statements---as combinations of simple, real-valued referential properties of predicates and their arguments. We use this framework to construct a dataset covering the entirety of the Universal Dependencies English Web Treebank. We use this dataset to probe the efficacy of type-level and token-level information---including hand-engineered features and static (GloVe) and contextual (ELMo) word embeddings---for predicting expressions of generalization. Data and code are available at decomp.io.
@article{arxiv.1901.11429,
title = {Decomposing Generalization: Models of Generic, Habitual, and Episodic Statements},
author = {Venkata Subrahmanyan Govindarajan and Benjamin Van Durme and Aaron Steven White},
journal= {arXiv preprint arXiv:1901.11429},
year = {2026}
}