We present an incremental Bayesian model for the unsupervised learning of syntactic categories from raw text. The model draws information from the distributional cues of words within an utterance, while explicitly bootstrapping its development on its own partiallylearned knowledge of syntactic categories. Testing our model on actual child-directed data, we demonstrate that it is robust to noise, learns reasonable categories, manages lexical ambiguity, and in general shows learning behaviours similar to those observed in children. 1

This position paper proposes that the study of embodied cognitive agents, such as humanoid robots, can advance our understanding of the cognitive development of complex sensorimotor, linguistic and social learning skills. This in turn will benefit the design of cognitive robots capable of learning to handle and manipulate objects and tools autonomously, to cooperate and communicate with other robots and humans, and to adapt their abilities to changing internal, environmental, and social conditions. Four key areas of research challenges are discussed, specifically for the issues related to the understanding of: (i) how agents learn and represent compositional actions; (ii) how agents learn and represent compositional lexicons; (iii) the dynamics of social interaction and learning; and (iv) how compositional action and language representations are integrated to bootstrap the cognitive system. The review of specific issues and progress in these areas is then translated into a practical roadmap based on a series of milestones. These milestones provide a possible set of cognitive robotics goals and test-scenarios, thus acting as a research roadmap for future work on cognitive developmental robotics.

constructions

Semantic roles are a critical aspect of linguistic knowledge because they indicate the relations of the participants in an event to the main predicate. Experimental studies on children and adults show that both groups use associations between general semantic roles such as Agent and Theme, and grammatical positions such as Subject and Object, even in the absence of familiar verbs. Other studies suggest that semantic roles evolve over time, and might best be viewed as a collection of verb-based or general semantic properties. A usage-based account of language acquisition suggests that general roles and their association with grammatical positions can be learned from the data children are exposed to, through a process of generalization and categorization. In this paper, we propose a probabilistic usage-based model of semantic role learning. Our model can acquire associations between the semantic properties of the arguments of an event, and the syntactic positions that the arguments appear in. These probabilistic associations enable the model to learn general conceptions of roles, based only on exposure to individual verb usages, and without requiring explicit labelling of the roles in the input. The acquired role properties are a good intuitive match to the expected properties of various roles, and are useful in guiding comprehension in the model to the most likely interpretation in the face of ambiguity. The learned roles can also be used to select the correct meaning of a novel verb in an ambiguous situation. 1

This paper presents an incremental probabilistic learner that models the acquistion of syntax and semantics from a corpus of child-directed utterances paired with possible representations of their meanings. These meaning representations approximate the contextual input available to the child; they do not specify the meanings of individual words or syntactic derivations. The learner then has to infer the meanings and syntactic properties of the words in the input along with a parsing model. We use the CCG grammatical framework and train a non-parametric Bayesian model of parse structure with online variational Bayesian expectation maximization. When tested on utterances from the CHILDES corpus, our learner outperforms a state-of-the-art semantic parser. In addition, it models such aspects of child acquisition as “fast mapping,” while also countering previous criticisms of statistical syntactic learners. 1

In addition to words and grammar, young children learn a large number of multiword sequences that are semantically idiosyncratic and have particular syntactic behaviour, e.g., expressions formed from the combination of a verb and a noun, such as take the train and give a kiss. Given the high degree of polysemy of verbs that commonly participate in such constructions, an important question is what cues children use to identify (nonliteral) multiword combinations. We provide evidence that certain statistical cues tapping into the properties of non-literal expressions are useful in separating these from literal combinations. Moreover, our experiments on naturally occurring child-directed data show that these cues are easily extractable from the input children receive.

We present an introduction to Bayesian inference as it is used in probabilistic models of cognitive development. Our goal is to provide an intuitive and accessible guide to the what, the how, and the why of the Bayesian approach: what sorts of problems and data the framework is most relevant for, and how and why it may be useful for developmentalists. We emphasize a qualitative understanding of Bayesian inference, but also include information about additional resources for those interested in the cognitive science applications, mathematical foundations, or machine learning details in more depth. In addition, we discuss some important interpretation issues that often arise when evaluating Bayesian models in cognitive science.

In this paper, we describe a model that learns semantic representations from the distributional statistics of language. This model, however, goes beyond the common bag-of-words paradigm, and infers semantic representations by taking into account the inherent sequential nature of linguistic data. The model we describe, which we refer to as a Hidden Markov Topics model, is a natural extension of the current state of the art in Bayesian bag-of-words models, that is, the Topics model of Griffiths, Steyvers, and Tenenbaum (2007), preserving its strengths while extending its scope to incorporate more fine-grained linguistic information.