Papers › Enhanced LSTM for Natural Language Inference
Enhanced LSTM for Natural Language Inference
Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, Si Wei, Hui Jiang, Diana Inkpen
Reasoning and inference are central to human and artificial intelligence. Modeling inference in human language is very challenging. With the availability of large annotated data (Bowman et al., 2015), it has recently become feasible to train neural network based inference models, which have shown to be very effective. In this paper, we present a new state-of-the-art result, achieving the accuracy of 88.6% on the Stanford Natural Language Inference Dataset. Unlike the previous top models that use very complicated network architectures, we first demonstrate that carefully designing sequential inference models based on chain LSTMs can outperform all previous models. Based on this, we further show that by explicitly considering recursive architectures in both local inference modeling and inference composition, we achieve additional improvement. Particularly, incorporating syntactic parsing information contributes to our best result---it further improves the performance even when added to the already very strong model.
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Code
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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 | 600D ESIM + 300D Syntactic TreeLSTM | % Test Accuracy | 88.6 | #33 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 600D ESIM + 300D Syntactic TreeLSTM | % Train Accuracy | 93.5 | #33 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 600D ESIM + 300D Syntactic TreeLSTM | Parameters | 7.7m | #33 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | Enhanced Sequential Inference Model (Chen et al., [2017a]) | % Test Accuracy | 88.0 | #41 of 98 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: ESIM
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