The goal of the project is to build a question answering system that works well on SQUAD dataset. The system should be able to read a paragraph and answer a question correctly related to the paragraph. This is an interesting task because it measures how well the system can interpret text. Reading Comprehension is an important field and being able to develop systems that can interpret text at human level will be able to lead us to the next revolution in Artificial Intelligence. The input to the system is a paragraph and a question related to the paragraph and the output from the system is the answer to the question based on the text in the paragraph. We have developed a system implementing character-level embedding using 1D Convolutions on top of the provided baseline code to mimic the BiDAF (Bidirectional Attention Flow) model. By adding the character-level embedding to the baseline starter code has given a lot of improvement to the EM and F1 scores. After running a lot of experiments, we found the best performing model to the one using an Adam optimizer with one char CNN embedding layer with Batch Normalization, learning rate of 0.0003 and dropout of 0.13. The scores received in the test leader-board are as follows: F1 - 66.174 and EM - 63.077.
