Papers › Stochastic Answer Networks for Natural Language Inference
Stochastic Answer Networks for Natural Language Inference
Xiaodong Liu, Kevin Duh, Jianfeng Gao
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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Natural Language Inference | SNLI | Stochastic Answer Network | % Test Accuracy | 88.5 | #35 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | Stochastic Answer Network | % Train Accuracy | 93.3 | #35 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | Stochastic Answer Network | Parameters | 3.5m | #35 of 98 | Archive leaderboard | report |
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