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In this work we have analyzed a novel concept of sequential binding basedlearning capable network based on the coupling of recurrent units with Bayesianprior definition. The coupling structure encodes to generate efficient tensorrepresentations that can be decoded to generate efficient sentences and candescribe certain events. These descriptions are derived from structuralrepresentations of visual features of images and media. An elaborated study ofthe different types of coupling recurrent structures are studied and someinsights of their performance are provided. Supervised learning performance fornatural language processing is judged based on statistical evaluations,however, the truth is perspective, and in this case the qualitative evaluationsreveal the real capability of the different architectural strengths andvariations. Bayesian prior definition of different embedding helps in bettercharacterization of the sentences based on the natural language structurerelated to parts of speech and other semantic level categorization in a formwhich is machine interpret-able and inherits the characteristics of the TensorRepresentation binding and unbinding based on the mutually orthogonality. Ourapproach has surpassed some of the existing basic works related to imagecaptioning.

CRUR: Coupled-Recurrent Unit for Unification, Conceptualization and  Context Capture for Language Representation -- A Generalization of Bi  Directional LSTM