On the other hand, convolution features embody only fix-sized local ngram information, whereas sentence-level feature aggregation via pooling can lead to loss of information (Sabour et al., 2017).
s-LsTM uses a global sentence-level node to assemble and back-distribute local information in the recurrent state transition process, suffering less information loss compared to pooling.