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TensorFlow Implementation of Neural Variational Inference for Text Processing

Neural Variational Document Model

Tensorflow implementation of Neural Variational Inference for Text Processing .

TensorFlow Implementation of Neural Variational Inference for Text Processing

This implementation contains:

  1. Neural Variational Document Model
    • Variational inference framework for generative model of text
    • Combines a stochastic document representation with a bag-of-words generative model
  2. Neural Answer Selection Model (in progress)
    • Variational inference framework for conditional generative model of text
    • Combines a LSTM embeddings with an attention mechanism to extract the semantics between question and answer

Prerequisites

Usage

To train a model with Penn Tree Bank dataset:

$ python main.py --dataset ptb 

To test an existing model:

$ python main.py --dataset ptb --forward_only True 

Results

Training details of NVDM. The best result can be achieved by onehost updates, not alternative updates.

TensorFlow Implementation of Neural Variational Inference for Text Processing

TensorFlow Implementation of Neural Variational Inference for Text Processing

Author

Taehoon Kim / @carpedm20

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