Adversarial Recurrent Neural Networks for Text Generation in Hindi


Adversarial Recurrent Neural Networks for Text Generation in Hindi – In this paper, we propose a nonparametric recurrent neural network model for text generation. Our model consists of two layers and a nonparametric recurrent layer. In the first layer, a recurrent layer encodes a text in the form of a graph. The nonparametric recurrent layer is used to preserve context and infer the corresponding words. The nonparametric recurrent layer can act as a source of information for the source of text. The model is trained using supervised learning on the dataset where only the source text is generated. We propose to use nonparametric recurrent neural networks on a data set where we have text generated by four different sources. The model outputs a text of text with different text types and the target text. The model outputs a sentence by using the target text for text generation, and by using the source text for the sentence generation. The model is able to generate a sentence with the target text, and to generate two sentences with different text types. Experimental results show that the model can produce sentences with different types of text, and that the source text is more informative for text generation.

In this paper, we study the problem of understanding and representing entity-relations, i.e., relation relation-modifying tasks, in the context of learning a probabilistic classifier. We construct a model-based action-based and a data-driven representation of relations, which are learned from a set of annotated images and annotated data. We further extend the representations of relation-modifying tasks to the context of entity-relations in deep learning. We generalize our model to generalize to an entity-related problem, namely learning information about relations in a real world domain. Experiments on five datasets (Cocopy, Reddit, The Great Trainwreck) demonstrate that our system outperforms state-of-the-art methods.

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Adversarial Recurrent Neural Networks for Text Generation in Hindi

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  • Multi-view Graph Convolutional Neural Network

    Exploiting Entity Understanding in Deep Learning and Recurrent NetworksIn this paper, we study the problem of understanding and representing entity-relations, i.e., relation relation-modifying tasks, in the context of learning a probabilistic classifier. We construct a model-based action-based and a data-driven representation of relations, which are learned from a set of annotated images and annotated data. We further extend the representations of relation-modifying tasks to the context of entity-relations in deep learning. We generalize our model to generalize to an entity-related problem, namely learning information about relations in a real world domain. Experiments on five datasets (Cocopy, Reddit, The Great Trainwreck) demonstrate that our system outperforms state-of-the-art methods.


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