In our implementation, the feature encoder G consists of three parts including a 300-dimensional word embedding layer using GloVe (Pennington et al., 2014), a one-layer CNN with ReLU activation function adopted in (Yu and Jiang, 2016;He et al., 2018) and a max-over-time pooling through which final sentence representation is obtained.
the convolution filter and the window size of this one-layer CNN are 300 and 3 separately.