Question-Aware Sentence Gating Networks for Question and Answering
Table 5: Performance comparisons over sentence matching and gating methods. Matching refers to the sentence matching module illustrated in section 3.2.
['[BOLD] Match-LSTM', '[BOLD] EM', '[BOLD] F1']
Sentence Matching and Gating Modules As for the combining word and sentence vectors, three methods are compared: 1) Concatenation: concatenating sentence and word representations, 2) Scalar gate: applying the same gating weight across dimensions, 3) Vector gate: our proposed method. Also, for the method without the sentence matching module, the output of the average pooling is used for a sentence vector. Among the two gating mechanisms, the vector gate achieves the highest EM and F1 scores along with the sentence matching module. Also, we can see that the sentence matching module improves the EM and F1 scores of SQuAD dataset, except for the case in conjunction with the concatenation. This indicates that with the proper manner of combining word and sentence, matching information between a passage and question sentence leads to a better model performance.