Stochastic Answer Networks for Natural Language Inference
This addresses the problem of complex reasoning in natural language understanding for AI researchers, though it appears incremental as it builds on existing NLI frameworks.
The paper tackles multi-step inference in Natural Language Inference by proposing a stochastic answer network (SAN) that iteratively refines predictions, achieving state-of-the-art results on SNLI, MultiNLI, and Quora Question Pairs datasets.
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.