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.
@article{arxiv.1804.07888,
title = {Stochastic Answer Networks for Natural Language Inference},
author = {Xiaodong Liu and Kevin Duh and Jianfeng Gao},
journal= {arXiv preprint arXiv:1804.07888},
year = {2019}
}