Decomposing Generalization: Models of Generic, Habitual, and Episodic Statements

Decomposing Generalization Models of Generic, Habitual, and Episodic Statements Venkata Govindarajan University of Rochester Benjamin Van Durme Johns Hopkins University Aaron Steven White University of Rochester Abstract We present a novel semantic framework for modeling linguistic expressions of generalization— generic, habitual, and episodic statements—as combinations of simple, real-valued referen- tial properties of predicates and their argu- ments. 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. 1 Introduction Natural language allows us to convey not only information about particular individuals and events, as in Example (1), but also generalizations about those individuals and events, as in (2). (1) a. Mary ate oatmeal for breakfast today. b. The students completed their assignments. (2) a. Mary eats oatmeal for breakfast. b. The students always complete their assign- ments on time. This capacity for expressing generalization is extremely flexible—allowing for generalizations about the kinds of events that particular individuals are habitually involved in, as in (2), as well as characterizations about kinds of things, as in (3). (3) a. Bishops move diagonally. b. Soap is used to remove dirt. Such distinctions between episodic statements (1), on the one hand, and habitual (2) and generic (or characterizing) statements (3), on the other, have a long history in both the linguistics and artificial intelligence literatures (see Carlson, 2011; Maienborn et al., 2011; Leslie and Lerner, 2016). Nevertheless, few modern semantic parsers make a systematic distinction (cf. Abzianidze and Bos, 2017). This is problematic, because the ability to accurately capture different modes of generaliza- tion is likely key to building systems with robust common sense reasoning (Zhang et al., 2017a; Bauer et al., 2018): Such systems need some source for general knowledge about the world (McCarthy, 1960, 1980, 1986; Minsky, 1974; Schank and Abelson, 1975; Hobbs et al., 1987; Reiter, 1987) and natural language text seems like a prime candidate. It is also surprising, because there is no dearth of data relevant to linguistic expressions of generalization (Doddington et al., 2004; Cybulska and Vossen, 2014b; Friedrich et al., 2015). One obstacle to further progress on general- ization is that current frameworks tend to take standard descriptive categories as sharp classes— for example, EPISODIC, GENERIC, HABITUAL for state- ments and KIND, INDIVIDUAL for noun phrases. This may seem reasonable for sentences like (1a), where Mary clearly refers to a particular individual, or (3a), where Bishops clearly refers to a kind; but natural text is less forgiving (Grimm, 2014, 2016, 2018). Consider the under- lined arguments in (4): Do they refer to kinds or individuals? (4) a. I will manage client expectations. b. The atmosphere may not be for everyone. c. Thanks again for great customer service! To remedy this, we propose a novel frame- work for capturing linguistic expressions of generalization. Taking inspiration from decompo- sitional semantics (Reisinger et al., …

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