Check code/rationale for the project of learning rationales for neural prediction:
Adam Yala has implemented a Pytorch version of the rationale project!
Check it out at: https://github.com/yala/text_nn
About this repo
This repo contains Theano implementations of popular neural network components and optimization methods. Source code of the following papers are also available:
 Molding CNNs for text: non-linear, non-consecutive convolutions. EMNLP 2015
 Semi-supervised Question Retrieval with Gated Convolutions. NAACL 2016
 Rationalizing Neural Predictions. EMNLP 2016
- Basic modules including feedforward layer, dropout, word embedding, RNN, LSTM, GRU and CNN
- Optimization methods including SGD, AdaGrad, AdaDelta and Adam
- Advanced modules from recent papers such as attention and gated convolution.
- Transparent to use GPU
1. Neural question retrieval for community-based QA
2. Sentiment analysis / document classification
3. Rationalizing neural predictions
Theano >= 0.7, Python >= 2.7, Numpy