deepakkumar1984 / QANet_keras

QANet in keras

QANet in keras

QANet: https://arxiv.org/abs/1804.09541

This keras model refers to QANet in tensorflow (https://github.com/NLPLearn/QANet). and the self-attention & position embedding are used from (https://kexue.fm/archives/4765, https://github.com/bojone/attention). Now the self-attention & position embedding are also revised from (https://github.com/NLPLearn/QANet).

We find that the conv based multi-head attention in (https://github.com/NLPLearn/QANet/blob/master/layers.py) performs 3%~4% better than the multiplying matrices based one in (https://github.com/bojone/attention/blob/master/attention_keras.py).

Pipline

1. Download squad data from (https://rajpurkar.github.io/SQuAD-explorer/).

2. Run preprocess.ipynb and handcraft.ipynb to get npys of the preprocessed data and handcraft features.

3. Run train_QANet.py to start training.

4. Fast demo: Use the god made model.fit() in QANet_fit_demo.py with random numpy data.

Updates

• Add EMA (with about 3% improvement)
• Add multi gpu (speed up)
• Support adding handcraft features
• Revised the MultiHeadAttention and PositionEmbedding in keras
• Support parallel multi-gpu training and inference
• Add layer dropout and revise the dropout bug (with about 2% improvement)
• Update the experimental results and related hyper-parameters (Coming soon)
• Revise the output Layer QAoutputBlock.py(with about 1% improvement)
• Add slice operation to QANet(get the max context length from each batch dynamically to speed up the model)
• Add data augmentation

I find that EMA in keras is hard to implement with GPU, and the training speed is greatly affected by it in keras. Besides, it's hard to add the slice op in keras too, so the training speed is further slower(cost about twice as much time compared with the optimized tensorflow version...). Moreover, there is also 2% gap of keras compared with the tensorflow version(https://github.com/NLPLearn/QANet).

Results

Result on dev set of squad