(2017), that max-pooling over BiLSTM hidden units outperforms average pooling operation on SNLI dataset.
with only 120K parameters, our SWEM-max achieves a test accuracy of 83.8%, which is very competitive among state-ofthe-art sentence encoding-based models (in terms of both performance and number of parameters) 1 .