Subtle Implicit Language Facts Emerge from the Functions of Constructions

Much has been written about the unlikelihood of innate, syntax-specific, universal knowledge of language (Universal Grammar) on the grounds that it is biologically implausible, unresponsive to cross-linguistic facts, theoretically inelegant, and implausible and unnecessary from the perspective of language acquisition. While relevant, much of this discussion fails to address the sorts of facts that generative linguists often take as evidence in favor of the Universal Grammar Hypothesis: subtle, intricate, knowledge about language that speakers implicitly know without being taught. This paper revisits a few often-cited such cases and argues that, although the facts are sometimes even more complex and subtle than is generally appreciated, appeals to Universal Grammar fail to explain the phenomena. Instead, such facts are strongly motivated by the functions of the constructions involved. The following specific cases are discussed: (a) the distribution and interpretation of anaphoric one, (b) constraints on long-distance dependencies,

Exploring the acquisition and production of grammatical constructions through human-robot interaction with echo state networks

One of the principal functions of human language is to allow people to coordinate joint action. This includes the description of events, requests for action, and their organization in time. A crucial component of language acquisition is learning the grammatical structures that allow the expression of such complex meaning related to physical events. The current research investigates the learning of grammatical constructions and their temporal organization in the context of human-robot physical interaction with the embodied sensorimotor humanoid platform, the iCub. We demonstrate three noteworthy phenomena. First, a recurrent network model is used in conjunction with this robotic platform to learn the mappings between grammatical forms and predicate-argument representations of meanings related to events, and the robot's execution of these events in time. Second, this learning mechanism functions in the inverse sense, i.e., in a language production mode, where rather than executing commanded actions, the robot will describe the results of human generated actions. Finally, we collect data from naïve subjects who interact with the robot via spoken language, and demonstrate significant learning and generalization results. This allows us to conclude that such a neural language learning system not only helps to characterize and understand some aspects of human language acquisition, but also that it can be useful in adaptive human-robot interaction.