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Context: All model hyperparameters were tuned on the respective validation sets of thetwo corpora.555For the Deep LSTM Reader, we consider hidden layer sizes{[64,128,\underline{256}]}, depths {[1,\underline{2},4]}, initiallearning rates {[1\text{\sc{e}}{-}3,5\text{\sc{e}}{-}4,\underline{1\text{\sc{e}}{-}4},5\text{\sc{e}}{-}5]}, batchsizes {[16,\underline{32}]} and dropout [0.0,\underline{0.1},0.2]. Weevaluate two types of feeds. In the cqa setup we feed first thecontext document and subsequently the question into the encoder, while theqca model starts by feeding in the question followed by the contextdocument. We report results on the best model (underlined hyperparameters,qca setup). For the attention models we consider hidden layer sizes[64,128,256], single layer, initial learning rates[1\text{\sc{e}}{-}4,5\text{\sc{e}}{-}5,2.5\text{\sc{e}}{-}5,1\text{\sc{e}}{-}5], batch sizes [8,16,32] and dropout[0,0.1,0.2,0.5]. For all models we used asynchronous RmsProp[20] with a momentum of 0.9 and a decay of 0.95.See Appendix A for more details of the experimental setup.Our experimental results are in Table 5, with theAttentive and Impatient Readers performing best across both datasets.

Question: Is hyperparameter optimization performed independently for the two dataset corpora?
Yes, it does appear that hyperparameter optimization for each dataset is performed independently.