Papers › DR-BiLSTM: Dependent Reading Bidirectional LSTM for Natural Language Inference
DR-BiLSTM: Dependent Reading Bidirectional LSTM for Natural Language Inference
Reza Ghaeini, Sadid A. Hasan, Vivek Datla, Joey Liu, Kathy Lee, Ashequl Qadir, Yuan Ling, Aaditya Prakash, Xiaoli Z. Fern, Oladimeji Farri
We present a novel deep learning architecture to address the natural language inference (NLI) task. Existing approaches mostly rely on simple reading mechanisms for independent encoding of the premise and hypothesis. Instead, we propose a novel dependent reading bidirectional LSTM network (DR-BiLSTM) to efficiently model the relationship between a premise and a hypothesis during encoding and inference. We also introduce a sophisticated ensemble strategy to combine our proposed models, which noticeably improves final predictions. Finally, we demonstrate how the results can be improved further with an additional preprocessing step. Our evaluation shows that DR-BiLSTM obtains the best single model and ensemble model results achieving the new state-of-the-art scores on the Stanford NLI 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 | 450D DR-BiLSTM Ensemble | % Test Accuracy | 89.3 | #19 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 450D DR-BiLSTM Ensemble | % Train Accuracy | 94.8 | #19 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 450D DR-BiLSTM Ensemble | Parameters | 45m | #19 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 450D DR-BiLSTM | % Test Accuracy | 88.5 | #34 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 450D DR-BiLSTM | % Train Accuracy | 94.1 | #34 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | 450D DR-BiLSTM | Parameters | 7.5m | #34 of 98 | Archive leaderboard | report |
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Methods
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