A Question-Focused Multi-Factor Attention Network for Question Answering
Table 7: Variation of m on the NewsQA development set.
['[BOLD] Value of  [ITALIC] m', '[BOLD] 1', '[BOLD] 2', '[BOLD] 3', '[BOLD] 4', '[BOLD] 5']
The out-of-vocabulary words are initialized with zero vectors. We use 50-dimension character-level embedding vectors. The number of hidden units in all the LSTMs is 150. During training, the minibatch size is fixed at 60. During testing, we enforce the constraint that the ending pointer will always be equal to or greater than the beginning pointer. We use exact match (EM) and F1 scores as the evaluation metrics. We use 4 factors for all the experiments as it gives the highest F1 score. Note that m=1 is equivalent to standard bilinear attention.