  We propose a stochastic answer network (SAN) to explore multi-step inference
strategies in Natural Language Inference. Rather than directly predicting the
results given the inputs, the model maintains a state and iteratively refines
its predictions. Our experiments show that SAN achieves the state-of-the-art
results on three benchmarks: Stanford Natural Language Inference (SNLI)
dataset, MultiGenre Natural Language Inference (MultiNLI) dataset and Quora
Question Pairs dataset.
