Question Answering is a interesting machine learning task which shows how machine can understand the relationship and the meaning of the words. There are lots of existing models built to solve this task. This paper draws inspiration from the paper Bidirectional Attention Flow for Machine Comprehension and dive deeper into the effect of character level embedding on the performance of the model. Through experimenting on different CNN model for character level embedding, we have concluded that a more complex CNN model does not result in a better performance metrics. However, through manually evaluate the model's prediction, we have found that a more complex model does perform better in certain cases.
