Pairwise Word Interaction Modeling with Deep Neural Networks for Semantic Similarity Measurement
Hua He, Jimmy Lin · 2016
Textual similarity measurement is a challenging problem, as it requires understanding the semantics of input sentences.Most previous neural network models use coarse-grained sentence modeling, which has difficulty capturing fine-grained word-level information for semantic comparisons.As an alternative, we propose to explicitly model pairwise word interactions and present a novel similarity focus mechanism to identify important correspondences for better similarity measurement.Our ideas are implemented in a novel neural network architecture that demonstrates state-ofthe-art accuracy on three SemEval tasks and two answer selection tasks.