  Current end-to-end machine reading and question answering (Q\&A) models are
primarily based on recurrent neural networks (RNNs) with attention. Despite
their success, these models are often slow for both training and inference due
to the sequential nature of RNNs. We propose a new Q\&A architecture called
QANet, which does not require recurrent networks: Its encoder consists
exclusively of convolution and self-attention, where convolution models local
interactions and self-attention models global interactions. On the SQuAD
dataset, our model is 3x to 13x faster in training and 4x to 9x faster in
inference, while achieving equivalent accuracy to recurrent models. The
speed-up gain allows us to train the model with much more data. We hence
combine our model with data generated by backtranslation from a neural machine
translation model. On the SQuAD dataset, our single model, trained with
augmented data, achieves 84.6 F1 score on the test set, which is significantly
better than the best published F1 score of 81.8.
