This paper is the introductory of Special Issue titled: “New Research on Role and Effects Structured Input in Assessing Nature Language Processing”. It provides a clear analysis nature role structured input activities second language research pedagogy. presents main findings genuine empirical investigating effectiveness different forms structures across languages among populations. The suggesti...

Under many conditions auditory input interferes with visual processing, especially early in development. These interference effects are often more pronounced when the auditory input is unfamiliar than when the auditory input is familiar (e.g. human speech, pre-familiarized sounds, etc.). The current study extends this research by examining how auditory input affects 8- and 14-month-olds' perfor...

Conditional random field (CRF) and Structural Support Vector Machine (Structural SVM) are two state-of-the-art methods for structured prediction which captures the interdependencies among output variables. The success of these methods is attributed to the fact that their discriminative models are able to account for overlapping features on the whole input observations. These features are usuall...

Cognitive scientists and developmental psychologists have suggested that development in perceptual, motor and memory functions of human infants as well as adaptive evaluation by caregivers facilitate learning for cognitive tasks by infants. This article presents a robotic approach to understanding the mechanism of how learning for joint attention can be helped by such functional development. A ...

Lexical analysis and parsing tasks, modeling deeper properties of the words and their relationships to each other, typically involve word segmentation, part-ofspeech tagging and parsing. A typical characteristic of such tasks is that the outputs have structured. All of them can fall into three types of structured prediction problems: sequence segmentation, sequence labeling and parsing. In this...

We present a new deterministic relational model derived from convolutional neural networks. Search-Convolutional Neural Networks (SCNNs) extend the notion of convolution to graph search to construct a rich latent representation that extracts local behavior from general graph-structured data. Unlike other neural network models that take graph-structured data as input, SCNNs have a parameterizati...