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.
REF propose to explicitly model pairwise word interactions with a pairwise word interaction similarity cube and a similarity focus layer to identify important word interactions.
Pairwise Word Interaction Modeling with Deep Neural Networks for Semantic Similarity Measurement
HLT-NAACL
Computer Science