  Representation-based Siamese networks have risen to popularity in lightweight
text matching due to their low deployment and inference costs. While word-level
attention mechanisms have been implemented within Siamese networks to improve
performance, we propose Feature Attention (FA), a novel downstream block
designed to enrich the modeling of dependencies among embedding features.
Employing "squeeze-and-excitation" techniques, the FA block dynamically adjusts
the emphasis on individual features, enabling the network to concentrate more
on features that significantly contribute to the final classification. Building
upon FA, we introduce a dynamic "selection" mechanism called Selective Feature
Attention (SFA), which leverages a stacked BiGRU Inception structure. The SFA
block facilitates multi-scale semantic extraction by traversing different
stacked BiGRU layers, encouraging the network to selectively concentrate on
semantic information and embedding features across varying levels of
abstraction. Both the FA and SFA blocks offer a seamless integration capability
with various Siamese networks, showcasing a plug-and-play characteristic.
Experimental evaluations conducted across diverse text matching baselines and
benchmarks underscore the indispensability of modeling feature attention and
the superiority of the "selection" mechanism.
