Task-Specific Attentive Pooling of Phrase AlignmentsContributes to Sentence MatchingEACL’2017 long paper
Table 3: Results on WikiQA. Significant improvement over both k-min-max-pooling and full-pooling is marked with ∗ (t-test, p < .05). STOA: 74.17 (MAP)/75.88 (MRR) in [Tymoshenko et al.2016]
['method Baselines', 'method CNN-Cnt', 'MAP 0.6520', 'MRR 0.6652']
Our improvement can be due to that: i) our linguistic units cover more exhaustive phrases, it enables alignments in a wider range; ii) we have two max-pooling steps in our attention pooling, especially the second one is able to remove some noisily aligned phrases. Both ABCNN and AP-CNN are based on convolutional layers, the phrase detection is constrained by filter sizes. Even though ABCNN tries a second CNN layer to detect bigger-granular phrases, their phrases in different CNN layers cannot be aligned directly as they are in different spaces. GRU in this work uses the same weights to learn representations of arbitrary-granular phrases, hence, all phrases can share the representations in the same space and can be compared directly.