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Neural Tree Indexers

Chainer implementation of Neural Tree Indexers for Text Understanding.

model_demo

This implementation contains:

  1. Full tree matching NTI-SLSTM-LSTM
    • Combines both recurrent sequential and recursive tree models
    • Performs tree matching with standart LSTM units
  2. Fast global and tree attentions described in the paper

Prerequisites

  • Python 2.7
  • chainer (tested on chainer 1.7.1 and 1.12.0)
  • Other data utils: sklearn, pandas, numpy etc.

Usage

To train a model with SNLI dataset:

$ python train_snli.py --snli path/to/snli_1.0 --glove path/to/glove.840B.300d.txt

Results

Full tree matching NTI-SLSTM-LSTM model with global attention achieves around 87.3% accuracy on Stanford NLI dataset.

Author

Tsendsuren Munkhdalai / @tsendeemts