Experimental results on SQuAD data set showed that our proposed approaches outperform the state-of-the-art BiDAF model, proving that the proposed embeddings play a significant part in correctly identifying answers for the machine comprehension task.
we found that our model can perform especially well on exact match metrics, which requires syntactic information to accurately locate the boundaries of the answers.