Genericity

Data

pred (zip), arg (zip)

About

An important line of study in formal semantics, philosophy, and AI investigates how language is used to represent knowledge of kinds, regularities and patterns. For instance, how do we know that lions roar describes a generalization about a kind of thing (lions), while those lions roared describes a specific event in which particular lions participated?

In this line of work, we propose a novel framework for capturing linguistic expressions of generalization. We suggest that linguistic expressions of generalization should be captured in a continuous multi-label system, rather than a multi-class system. We do this by decomposing categories such as EPISODIC, HABITUAL, and GENERIC into simple referential properties of predicates and their arguments.

For a detailed description of the protocols, datasets as well as models of these data, please see the following paper:

V. S. Govindarajan, B. Van Durme, & White, A. S. 2019. Decomposing Generalization: Models of Generic, Habitual, and Episodic Statements.

The code for generating the dataset files for Amazon Mechanical Turk (along with the stylesheets) can be found here. The code for the experiments described in the paper can be found here

Researchers

Benjamin Van Durme bio photo
Benjamin Van Durme
Aaron Steven White bio photo
Aaron Steven White
Venkata Subrahmanyan G bio photo
Venkata Subrahmanyan G