Seq2seq-Attention Question Answering Model

A sequence-to-sequence attention reading comprehension model was implemented to fulfill Question Answering task defined in Stanford Question Answering Dataset (SQuAD). The basic structure was bidirectional LSTM (BiLSTM) encodings with attention mechanism as well as BiLSTM decoding. Several adjustments such as dropout, learning rate decay, and gradients clipping were used. Finally, the model achieved 57.8% F1 score and 47.5% Exact Match (EM) ratio on validation set; and 49.1% F1 and 35.9% EM on private test set. Future work concerns improvement on preventing overfitting while adding hidden layers.