Dynamic Coattention Networks for Question Answering
Table 2: Single model ablations on the development set.
['Model', 'Dev EM', 'Dev F1']
Model Ablation On the decoder side, we experiment with various pool sizes for the HMN maxout layers, using a 2-layer MLP instead of a HMN, and forcing the HMN decoder to a single iteration. Empirically, we achieve the best performance on the development set with an iterative HMN with pool size 16, and find that the model consistently benefits from a deeper, iterative decoder network. The performance improves as the number of maximum allowed iterations increases, with little improvement after 4 iterations. This suggests that, at an additional cost of a softmax computation and a dot product, the coattention mechanism provides a simple and effective means to better encode the document and question sequences. Further studies, such as performance without attention and performance on questions requiring different types of reasoning can be found in the appendix.