# DR-BiLSTM: Dependent Reading Bidirectional LSTM for Natural Language Inference

arXiv: https://arxiv.org/abs/1802.05577 · HTML: https://mlsanity.com/paper/1802.05577/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Feb 2018.

## Built on by (most cited first, 5 of 5)

- [Topic-Aware Evidence Reasoning and Stance-Aware Aggregation for Fact Verification](https://mlsanity.com/paper/2106.01191/) (2021)
- [A Neural-Symbolic Approach to Natural Language Understanding](https://mlsanity.com/paper/2203.10557/) (2022)
- [Testing Pre-trained Language Models' Understanding of Distributivity via Causal Mediation Analysis](https://mlsanity.com/paper/2209.04761/) (2022)
- [Multi-Evidence based Fact Verification via A Confidential Graph Neural Network](https://mlsanity.com/paper/2405.10481/) (2024)
- [A deep Natural Language Inference predictor without language-specific training data](https://mlsanity.com/paper/2309.02887/) (2023)

## Abstract (arXiv)

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.