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???This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." ???Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.???We present a case-study analysing the prosodic contours and salient word markers of a small corpus of robot-directed speech where the human participants had been asked to talk to a socially interactive robot as if it were a child. We assess whether such contours and salience characteristics could be used to extract relevant information for the subsequent learning and scaffolding of meaning in robots. The study uses measures of pitch, energy and word duration from the participants speech and exploits Pierrehumbert and Hirschberg's theory of the meaning of intonational contours which may provide information on shared belief between speaker and listener. The results indicate that 1) participants use a high number of contours which provide new information markers to the robot, 2) that prosodic question contours reduce as the interactions proceed and 3) that pitch, energy and duration features can provide strong markers for relevant words and 4) there was little evidence that participants altered their prosodic contours in recognition of shared belief. A description and verification of our software which allows the semi-automatic marking of prosodic phrases is also described

We describe a method for learning an incremental semantic grammar from a corpus in which sentences are paired with logical forms as predicate-argument structure trees. Working in the framework of Dynamic Syntax, and assuming a set of generally available compositional mechanisms, we show how lexical entries can be learned as probabilistic procedures for the incremental projection of semantic structure, providing a grammar suitable for use in an incremental probabilistic parser. By inducing these from a corpus generated using an existing grammar, we demonstrate that this results in both good coverage and compatibility with the original entries, without requiring annotation at the word level. We show that this semantic approach to grammar induction has the novel ability to learn the syntactic and semantic constraints on pronouns. * We would like to thank Ruth Kempson and Yo Sato for helpful comments and discussion.

This article presents results from a multidisciplinary research project on the integration and transfer of language knowledge into robots as an empirical paradigm for the study of language development in both humans and humanoid robots. Within the framework of human linguistic and cognitive development, we focus on how three central types of learning interact and co-develop: individual learning about one's own embodiment and the environment, social learning (learning from others), and learning of linguistic capability. Our primary concern is how these capabilities can scaffold each other's development in a continuous feedback cycle as their interactions yield increasingly sophisticated competencies in the agent's capacity to interact with others and manipulate its world. Experimental results are summarized in relation to milestones in human linguistic and cognitive development and show that the mutual scaffolding of social learning, individual learning, and linguistic capabilities creates the context, conditions, and requisites for learning in each domain. Challenges and insights identified as a result of this research program are discussed with regard to possible and actual contributions to cognitive science and language ontogeny. In conclusion, directions for future work are suggested that continue to develop this approach toward an integrated framework for understanding these mutually scaffolding processes as a basis for language development in humans and robots.

This paper argues that by analysing language as a mechanism for growth of information (Cann et al. in The Dynamics of Language, Elsevier, Oxford, 2005; Kempson et al. in Dynamic Syntax, Blackwell, Oxford, 2001), not only does a unitary basis for ellipsis become possible, otherwise thought to be irredeemably heterogeneous, but also a whole range of sub-types of ellipsis, otherwise thought to be unique to dialogue, emerge as natural consequences of use of language in context. Dialogue fragment types modelled include reformulations, clarification requests, extensions, and acknowledgements. Buttressing this analysis, we show how incremental use of fragments serves to progressively narrow down the otherwise mushrooming interpretational alternatives in language use, and hence is central to fluent conversational interaction. We conclude that, by its ability to reflect dialogue dynamics as a core phenomenon of language use, a grammar with inbuilt parsing dynamics opens up the potential for analysing language as a mechanism for communicative interaction.