Second, the measure makes sense theoretically, both from algorithmic and native language acquisition points of view.

We also make an interesting observation that the impressionistic evaluation of syntactic complexity is better approximated by the presence or absence of grammar and usage patterns (and not by their frequency of occurrence), an idea supported by studies in native language acquisition .

However, when considered in the context of language acquisition studies, this approach seems to be justified.

Studies in native language acquisition, have considered multiple grammatical developmental indices that represent the grammatical levels reached at various stages of language acquisition .

Similarly, Scarborough (1990) proposed the Index of Productive Syntax (IPSyn), according to which, the presence of particular grammatical structures, from a list of 60 structures (ranging from simple ones such as including only subjects and verbs, to more complex constructions such as conjoined sentences) is evidence of language acquisition milestones.

Prior studies in language acquisition and second language research have conclusively shown that proficiency in a second language is characterized by several factors, some of which are, fluency in language production, pronunciation accuracy, choice of vocabulary, grammatical sophistication and accuracy.

0 In the domain of native language acquisition , the presence or absence of a grammatical structure indicates grammatical development.

Informed by studies in second language acquisition and language testing that regard these factors as key determiners of spoken language proficiency, some researchers have focused on the objective measurement of these aspects of spoken language in the context of automatic assessment of language ability.

The idea that the level of syntactic complexity (in terms of its range and sophistication) can be assessed based on the distribution of POS-tags is informed by prior studies in second language acquisition .

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.

This paper uses an unsupervised model of grounded language acquisition to study the role that social cues play in language acquisition .

From learning sounds to learning the meanings of words, social interactions are extremely important for children’s early language acquisition (Baldwin, 1993; Kuhl et al., 2003).

In order to study the role that social cues play in language acquisition , this paper presents a structured statistical model of

Thus, this work is consistent with a view of language acquisition in which children learn to learn, discovering organizing principles for how language is organized and used socially (Baldwin, 1993; Hollich et al., 2000; Smith et al., 2002).

Of course this work only scratches the surface in terms of investigating the role of function words in language acquisition .

Over the past two decades psychologists have investigated the role that function words might play in human language acquisition .

The goal of this paper is to determine whether computational models of human language acquisition can provide support for the hypothesis that

function words are treated specially in human language acquisition .

A central problem in grounded language acquisition is learning the correspondences between a rich world state and a stream of text which references that world state.

6It is interesting to note that this type of staged training is evocative of language acquisition in children: lexical associations are formed (Model 1) before higher-level discourse structure is learned (Model 3).

However, this degree of supervision is unrealistic for modeling human language acquisition and can be costly to obtain for building large-scale, broad-coverage language understanding systems.

A more flexible direction is grounded language acquisition : learning the meaning of sentences in the context of an observed world state.

This suggests that WordEnds is a viable model of child language acquisition and might be useful in speech understanding.

This sets a much higher standard for models of child language acquisition and also suggests that it is not crazy to speculate about inserting such an algorithm into the speech recognition pipeline.

Moreover, understanding such speech is the end goal of child language acquisition .

In this paper we present experiments with an automatic system for semantic role labeling (SRL) that is designed to model aspects of human language acquisition .

Proposed solutions to this problem in the NLP and human language acquisition literatures focus on distributional learning as a key data source (e.g., (Mintz, 2003; Johnson, 2007)).

The architecture of our system is similar to a previous approach to modeling early language acquisition (Connor et al., 2009), which is itself based on the standard architecture of a full SRL system (e.g.

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.

If true, this fact would have striking implications for theories and models of language acquisition , as well as numerous applications in natural language processing.

It is frequently invoked in theories of language acquisition .

It has also been employed in models of language acquisition (Parisien and Stevenson, 2011; Barak et al., 2012).