Attentive Pooling Networks
Table 2: Neural Network Hyper-Parameters
['[BOLD] Hyp.', '[BOLD] Hyperpar. Name', '[BOLD] AP-CNN', '[BOLD] QA-CNN', '[BOLD] AP-biLSTM', '[BOLD] QA-biLSTM']
We try to use as much as possible the same hyperparameters for all the three datasets. The size of the word embeddings is different due to the different pre-trained versions that we used for InsuranceQA and the other two datasets. We use a context window of size 3 for InsuranceQA, while we set this parameter to 4 for TREC-QA and WikiQA. Using the selected hyperparameters, the best results are normally achieved using between 15 and 25 training epochs. For AP-CNN, AP-biLSTM and QA-LSTM, we also use a learning rate schedule that decreases the learning rate λ according to the training epoch t.