Exploring Combinations of Character Embeddings and Coattention

In this project, I attempt to build a model for the Stanford Question AnsweringDataset (SQuAD) v. 2.0 [1]. I consider 3 different models, the baseline model,or Bi-directional Attention Flow (BiDAF) without character level embedding [2],BiDAF with character level embedding, and a Dynamic Co-attention Network [3]with character level embedding. Some conclusions drawn from my experiment wasthat implementing character level embedding in the BiDAF model significantlyimproved EM and F1 scores over the baseline. However, even though the DynamicCo-Attention Network with character level embedding was an improvement overthe baseline, it scored lower on both F1 and EM scores than BiDAF with characterlevel embedding. On the development set, the BiDAF with character embeddinghas an F1 score of 63.030 and EM score of 59.839. The Dynamic Co-attentionNetwork with character embedding has an F1 score of 61.54 and an EM of 57.81.My best result on the SQuAD testing set was the BiDAF with character embeddings,achieving an F1 score of 62.266 and an EM score of 58.952.