A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations
Table 2: The effect of pooling parameter k on QA.
['[EMPTY]', 'P@1', 'MRR']
In this experiment, we report different results when k is set to 1, 3, 5 and 10. It means that multiple sentence representations do help matching. We also observe that when k is larger than 5, the improvement is quite limited. Therefore, we set k to 5 in the following experiments. We further compare our model with LSTM-RNN and Bi-LSTM-RNN, where they use LSTM from one and two directions to generate sentence representations, respectively. That is to say, LSTM-RNN views the matching problems as matching at the last position, while Bi-LSTM-RNN both leverage matchings at the first and last position. The results indicate that the best matching position is not always in the first or last one. Therefore, the consideration of multiple positional sentence representations is necessary.