Note that all four proposed strategies can be easily extended to other pre-trained general-purpose sentence embeddings as well.
a bidirectional LSTM architecture along with a max-pooling operation over the hidden units is employed as the sentence encoder, and the model parameters are optimized on the natural language inference tasks, i.e., Standford Natural Language Inference (SNLI) (Bowman et al., 2015) and Multi-Genre Natural Language Inference (MultiNLI) datasets (Williams et al., 2017).