Recurrent neural networks (RNNs) process input text sequentially and model the conditional transition between word tokens. In contrast, the advantages of recursive networks include that they explicitly model the compositionality and the recursive structure of natural language. However, the current recursive architecture is limited by its dependence on syntactic tree. In this paper, we introduce...

CABINS is a framework of modeling an optimization task in illstructured domains. In such domains, neither systems nor human experts possess the exact model for guiding optimization. And the user's model of optimality is subjective and situation-dependent. CABINS optimizes a solution through iterative revision using case-based reasoning. In CABINS, task structure analysis was adopted for creatin...

A common obstacle preventing the rapid deployment of supervised machine learning algorithms is the lack of labeled training data. This is particularly expensive to obtain for structured prediction tasks, where each training instance may have multiple, interacting labels, all of which must be correctly annotated for the instance to be of use to the learner. Traditional active learning addresses ...

The present study assessed the construct validity and reliability of a researcher-constructed psycho-motor mechanism scale based on the communicative theory of translation proposed by PACTE (2003). In doing so, the necessary criteria for designing the scale were obtained by a thorough review of related literature on previously constructed scales in error analysis or holistic ones. Moreover, in ...

The present study assessed the construct validity and reliability of a researcher-constructed psycho-motor mechanism scale based on the communicative theory of translation proposed by PACTE (2003). In doing so, the necessary criteria for designing the scale were obtained by a thorough review of related literature on previously constructed scales in error analysis or holistic ones. Moreover, in ...

We consider recurrent neural networks which deal with symbolic formulas, terms, or, generally speaking, tree-structured data. Approaches like the recursive autoassociative memory, discrete-time recurrent networks, folding networks, tensor construction, holographic reduced representations, and recursive reduced descriptions fall into this category. They share the basic dynamics of how structured...

abstract the present study examined the effect of genre-based tasks on efl learners' speaking performance and probed whether genre-based tasks may empower efl learners to perform better on speaking tests. a further concern of the study was to explore whether the effect of genre-based tasks on speaking ability of efl learners varied across different age groups, i.e. teenagers (13-16 years o...

A framwork for Natural Language Generation (NLG) is described, that can be used with arbitrarily structured knowledge sources (for example a database). It is built around three layers of domain knowledge representation , supporting diierent tasks. The beneet of this approach is that a given representation can be used as input to NLG rather than a redundant representation that has to be maintain...

Detection and isolation of faults are important tasks for improving the reliability of process industries in order to enhance their products quality. This paper investigates the detection and isolation of faults using structured residuals. Actuator and sensor faults are considered. Residuals are generated using a bank of unknown input observers (UIO). Three-tank benchmark system was used as a p...