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Enhanced LSTM for Natural Language Inference

arXiv 1609.06038 ↗First posted 20 Sep 2016cs.CLcited by 199

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Abstract (arXiv)

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.

Key facts. Cited by 199 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Sep 2016.

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Citing papers per year: 2021: 73; 2022: 46; 2023: 35; 2024: 27; 2025: 13; 2026: 5.

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  1. 2022A Survey of Controllable Text Generation using Transformer-based Pre-trained Language Modelscited by 128
  2. 2021LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relationscited by 119
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  5. 2021DocNLI: A Large-scale Dataset for Document-level Natural Language Inferencecited by 75
  6. 2021CoSQA: 20,000+ Web Queries for Code Search and Question Answeringcited by 74
  7. 2021Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristicscited by 67
  8. 2021ZS-BERT: Towards Zero-Shot Relation Extraction with Attribute Representation Learningcited by 46
  9. 2021Coreference Resolution without Span Representationscited by 45
  10. 2021BERT & Family Eat Word Salad: Experiments with Text Understandingcited by 44
  11. 2021Tracing Text Provenance via Context-Aware Lexical Substitutioncited by 40
  12. 2021Paradigm Shift in Natural Language Processingcited by 39
38 more
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  2. 2021Improving Cross-Modal Alignment in Vision Language Navigation via Syntactic Informationcited by 31
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  4. 2023Revisiting Large Language Models as Zero-shot Relation Extractorscited by 29
  5. 2022Faithfulness in Natural Language Generation: A Systematic Survey of Analysis, Evaluation and Optimization Methodscited by 27
  6. 2023Pushing the Limits of ChatGPT on NLP Taskscited by 27
  7. 2021Bayesian Attention Belief Networkscited by 25
  8. 2021NeuralLog: Natural Language Inference with Joint Neural and Logical Reasoningcited by 24
  9. 2021Using Prior Knowledge to Guide BERT's Attention in Semantic Textual Matching Taskscited by 22
  10. 2021QuantumFed: A Federated Learning Framework for Collaborative Quantum Trainingcited by 22
  11. 2022DABERT: Dual Attention Enhanced BERT for Semantic Matchingcited by 21
  12. 2023Dual Path Modeling for Semantic Matching by Perceiving Subtle Conflictscited by 21
  13. 2021SPECTRA: Sparse Structured Text Rationalizationcited by 19
  14. 2022Why is constrained neural language generation particularly challenging?cited by 19
  15. 2021Don't be Contradicted with Anything! CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue Systemcited by 18
  16. 2021Hierarchical Cross-Modality Semantic Correlation Learning Model for Multimodal Summarizationcited by 18
  17. 2022MENLI: Robust Evaluation Metrics from Natural Language Inferencecited by 18
  18. 2022Improving Semantic Matching through Dependency-Enhanced Pre-trained Model with Adaptive Fusioncited by 18
  19. 2022On the Calibration of Massively Multilingual Language Modelscited by 18
  20. 2023Natural Language Decompositions of Implicit Content Enable Better Text Representationscited by 18
  21. 2021Hierarchical Multi-head Attentive Network for Evidence-aware Fake News Detectioncited by 17
  22. 2021The Role of Context in Detecting Previously Fact-Checked Claimscited by 17
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  24. 2022Improving Personality Consistency in Conversation by Persona Extendingcited by 17
  25. 2024Quantum Kernel-Based Long Short-term Memory for Climate Time-Series Forecastingcited by 17
  26. 2021Topic-Aware Evidence Reasoning and Stance-Aware Aggregation for Fact Verificationcited by 16
  27. 2021Uncertainty Calibration for Ensemble-Based Debiasing Methodscited by 16
  28. 2021LET: Linguistic Knowledge Enhanced Graph Transformer for Chinese Short Text Matchingcited by 15
  29. 2021Enhancing Backdoor Attacks with Multi-Level MMD Regularizationcited by 15
  30. 2021Alignment Attention by Matching Key and Query Distributionscited by 14
  31. 2023Zero-shot Transfer of Article-aware Legal Outcome Classification for European Court of Human Rights Casescited by 14
  32. 2021A Multi-Level Attention Model for Evidence-Based Fact Checkingcited by 13
  33. 2022Paraphrase Identification with Deep Learning: A Review of Datasets and Methodscited by 13
  34. 2022Attentive Deep Neural Networks for Legal Document Retrievalcited by 13
  35. 2021Reward Optimization for Neural Machine Translation with Learned Metricscited by 12
  36. 2022Pair-Level Supervised Contrastive Learning for Natural Language Inferencecited by 12
  37. 2024Lessons from the Use of Natural Language Inference (NLI) in Requirements Engineering Taskscited by 12
  38. 2022Fact-Saboteurs: A Taxonomy of Evidence Manipulation Attacks against Fact-Verification Systemscited by 11

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