Modeling Verb Lexicalization Biases using Hierarchical Bayesian Models.

Modeling Verb Lexicalization Biases using Hierarchical Bayesian Models Catherine Havasi (havasi@media.mit.edu) MIT Media Lab, 20 Ames Street Cambridge, MA 02139 USA Robert Speer (rspeer@mit.edu) MIT Media Lab, 20 Ames Street Cambridge, MA 02139 USA Abstract a single example (Gentner & Boroditsky, 2001), and they can even learn words for events they are unable to observe (Landau & Gleitman, 1985). Two faster and more noise-resistant strategies have been hypothesized by researchers. One is syntactic bootstrapping (Gleitman, 1990). In this theory, the syntactic frame of the verb is used to constrain hypotheses to those which makes sense in the given frame and are similar to known verbs with similar frames. In the manner/path example given earlier, you would be more likely to think the meaning of the novel verb was related to its motion if you had heard the semantically rich fame “Jesse gorped the frisbee to Edison.” Another hypothesis is that we are able to quickly learn words from few examples because we rely on our learned lexicalization biases about the meanings of words (Gentner & Boroditsky, 2001). Learners select word meanings that align with the features that are dominant in the learner’s native language (Naigles, 1990), indicating that language learners observe general features of the meanings of other words and apply them to new words as well. Modern evidence suggests that children use a combination of these strategies (Papafragou & Selimis, 2010). But how are these biases learned and regulated? In this paper, we explore the possibility that biases for certain components of meaning are associated with language and semantic frame. These biases represent examples of Bayesian overhypotheses about what a word is likely to mean, and these overhypotheses can themselves be learned from examples (Kemp, Perfors, & Tenenbaum, 2007). The overhypotheses can depend on observable features such as whether the referent is animate (Smith, Jones, Landau, Gershkoff-Stowe, & Samuelson, 2002), the syntactic patterns in which the word appears (Cifuentes- F´erez & Gentner, 2006), or known lexical relations to other words (Pustejovsky, 1998). Return momentarily to the analogous results for shape bi- ases in nouns — that early nouns that children learn tend to be easily clustered by the shape of their referents. In order to model this bias, it was postulated that children learn a “second- order generalization” that objects are often categorized by their shape (Samuelson & Smith, 1999). Smith et al. demonstrated this generalization by teaching 17-19 month old children a precocious shape bias (Smith et al., 2002). Kemp, Perfors, and Tenenbaum explained this kind of learning using a hierarchical Bayesian model, which could learn both base meanings and overhypotheses simultaneously (Kemp et al., 2007) and cases The expression of motion verbs differs between languages. The path of motion, such as crossing or entering, is more promi- nently featured in path-based languages such as Spanish than in manner-based languages such as English. Here, we revisit the data from a study on manner and path biases in verb lexi- calization (Havasi & Snedeker, 2004), and create a hierarchical Baysian computational model to further explore, verify, and define these biases. With this model, we can discover the large differences in subjects’ pre-existing manner and path biases that depend on the syntactic frame in which new verbs appear, as well as a difference in the learning rate between English speakers taking the experiment in English and bilingual Span- ish speakers taking the experiment in Spanish. We can also use the model to predict the responses of subjects in the experiment with more accuracy than before. Keywords: verb learning; bayesian modeling; hierarchical Bayes modeling; manner and path verbs Linguistic lexicalization biases People have the ability to intuit the meaning of a new verb after hearing it used to describe just a single event. In the case of a novel verb, there are many potential hypotheses of the verb’s meaning which may be consistent with the event witnessed. Suppose you hear a novel verb, such as “gorp”, being used to describe an event in which Jesse throws a frisbee across a field to , her dog. The verb could refer to Jesse throwing the frisbee, the frisbee’s motion as it glides across the field, the frisbee’s traverse of the field, or Edison’s act of catching the frisbee. To understand which aspect of the action the verb refers to, you must use situational clues and background knowledge. When one encounters a new object noun, one encounters the same ambiguity in meaning. In practice, languages sys- tematically favor a few different characteristics such as com- mon ancestry or base level category (Nelson, 1973) for noun meanings which is often indicated by shape. However, event categorization tends to be flexible across languages and even with a language (Talmy, 1975). A motion verb, for example, could easily refer to the manner, cause, or path of the motion with no universal preference across languages (Aske, 1989; Berman & Slobin, 1994; Jackendoff, 1990). Given the plethora of possible referents for a novel verb, how do children learn verb meanings? One solution would be to observe, over several examples, that certain semantic features seem to always be present and are thus associated with the verb’s meaning. However, this would require too much data to match the way that children learn words; children can often determine the relevant aspect of a word’s meaning from