Hierarchical Bayesian Models (HBMs) have been used with some success to capture empirically observed patterns of under- and overgeneralization in child language acquisition .

This paper presents such an evaluation for a language acquisition domain where explicit HBMs have been proposed: the acquisition of English dative constructions.

HBMs have been successfully used for a number of language acquisition tasks capturing both patterns of under- and overgeneralization found in child language acquisition .

A familiar problem for language acquisition is how children learn which verbs participate in so-called dative alternations, exemplified by the child-produced sentences 1 to 3, from the Brown (1973) corpus in CHILDES (MacWhin-ney, 1995).

In recent years, with advances in probability and estimation theory, there has been much interest in Bayesian models (BMs) (Chater, Tenenbaum, and Yuille, 2006; Jones and Love, 2011) and their application to child language acquisition with its challenging com-

In the case of many language acquisition tasks this behavior often takes the form of overgeneralization, but with eventual convergence to some target language given exposure to more data.

This paper is organized as follows: we start with a discussion of formalizations of language acquisition tasks, §2.

To emulate a child language acquisition environment we use naturalistic longitudinal child-directed data, from the Brown corpus in CHILDES, for one child (Adam) for a subset of 19 verbs in the DOD and PD verb frames, figure 1.

We adapt discriminative reranking to improve the performance of grounded language acquisition , specifically the task of learning to follow navigation instructions from observation.

Grounded language acquisition involves learning to comprehend and/or generate language by simply observing its use in a naturally occurring context in which the meaning of a sentence is grounded in perception and/or action (Roy, 2002; Yu and Ballard, 2004; Gold and Scassel-lati, 2007; Chen et al., 2010).

However, to our knowledge, there has been no previous attempt to apply discriminative reranking to grounded language acquisition , where gold-standard reference parses are not typically available for training reranking models.