Papers › Learning Natural Language Inference using Bidirectional LSTM model and Inner-Attention
Learning Natural Language Inference using Bidirectional LSTM model and Inner-Attention
Yang Liu, Chengjie Sun, Lei Lin, Xiaolong Wang
In this paper, we proposed a sentence encoding-based model for recognizing text entailment. In our approach, the encoding of sentence is a two-stage process. Firstly, average pooling was used over word-level bidirectional LSTM (biLSTM) to generate a first-stage sentence representation. Secondly, attention mechanism was employed to replace average pooling on the same sentence for better representations. Instead of using target sentence to attend words in source sentence, we utilized the sentence's first-stage representation to attend words appeared in itself, which is called "Inner-Attention" in our paper . Experiments conducted on Stanford Natural Language Inference (SNLI) Corpus has proved the effectiveness of "Inner-Attention" mechanism. With less number of parameters, our model outperformed the existing best sentence encoding-based approach by a large margin.
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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 (300+300) BiLSTM encoders with intra-attention and symbolic preproc. | % Test Accuracy | 85.0 | #74 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 600D (300+300) BiLSTM encoders with intra-attention and symbolic preproc. | % Train Accuracy | 85.9 | #74 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 600D (300+300) BiLSTM encoders with intra-attention and symbolic preproc. | Parameters | 2.8m | #74 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 600D (300+300) BiLSTM encoders with intra-attention | % Test Accuracy | 84.2 | #81 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 600D (300+300) BiLSTM encoders with intra-attention | % Train Accuracy | 84.5 | #81 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 600D (300+300) BiLSTM encoders with intra-attention | Parameters | 2.8m | #81 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 600D (300+300) BiLSTM encoders | % Test Accuracy | 83.3 | #85 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 600D (300+300) BiLSTM encoders | % Train Accuracy | 86.4 | #85 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 600D (300+300) BiLSTM encoders | Parameters | 2.0m | #85 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
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