A Question-Focused Multi-Factor Attention Network for Question Answering
Table 8: Variation of question aggregation formulation on the NewsQA development set.
['[BOLD] Aggregation', '[BOLD] EM', '[BOLD] F1']
For mean aggregation, the attentional weight vector k is formulated by applying column-wise averaging on the attention matrix A. Intuitively, it is giving equal priority to all the passage words to determine a particular question word attention. Similarly, in the case of sum aggregation, we apply a column-wise sum operation. , it is helping to give higher weights to the more important question words based on the most relevant passage words.