  Applying neural-networks on Question Answering has gained increasing
popularity in recent years. In this paper, I implemented a model with
Bi-directional attention flow layer, connected with a Multi-layer LSTM encoder,
connected with one start-index decoder and one conditioning end-index decoder.
I introduce a new end-index decoder layer, conditioning on start-index output.
The Experiment shows this has increased model performance by 15.16%. For
prediction, I proposed a new smart-span equation, rewarding both short answer
length and high probability in start-index and end-index, which further
improved the prediction accuracy. The best single model achieves an F1 score of
73.97% and EM score of 64.95% on test set.
