mlsanityData to 14 Oct 2026

NeuralLog: Natural Language Inference with Joint Neural and Logical Reasoning

arXiv 2105.14167 ↗First posted 29 May 2021cs.CL cs.AIcited by 25

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Abstract (arXiv)

Deep learning (DL) based language models achieve high performance on various benchmarks for Natural Language Inference (NLI). And at this time, symbolic approaches to NLI are receiving less attention. Both approaches (symbolic and DL) have their advantages and weaknesses. However, currently, no method combines them in a system to solve the task of NLI. To merge symbolic and deep learning methods, we propose an inference framework called NeuralLog, which utilizes both a monotonicity-based logical inference engine and a neural network language model for phrase alignment. Our framework models the NLI task as a classic search problem and uses the beam search algorithm to search for optimal inference paths. Experiments show that our joint logic and neural inference system improves accuracy on the NLI task and can achieve state-of-art accuracy on the SICK and MED datasets.

Key facts. Cited by 25 papers in the map (data through 14 Oct 2026). Builds on 22 of its 36 references that are in the map. First posted on arXiv 29 May 2021.

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22 of 36 references.

References · 22

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  3. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  4. 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,790
  5. 2017SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluationcited by 1,001
  6. 2019Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inferencecited by 876
  7. 2020Stanza: A Python Natural Language Processing Toolkit for Many Human Languagescited by 723
  8. 2018Annotation Artifacts in Natural Language Inference Datacited by 671
  9. 2016A Decomposable Attention Model for Natural Language Inferencecited by 215
  10. 2016Enhanced LSTM for Natural Language Inferencecited by 200
  11. 2018Breaking NLI Systems with Sentences that Require Simple Lexical Inferencescited by 134
  12. 2019SenseBERT: Driving Some Sense into BERTcited by 73
10 more
  1. 2019Can neural networks understand monotonicity reasoning?cited by 45
  2. 2019HELP: A Dataset for Identifying Shortcomings of Neural Models in Monotonicity Reasoningcited by 35
  3. 2019MonaLog: a Lightweight System for Natural Language Inference Based on Monotonicitycited by 21
  4. 2017LangPro: Natural Language Theorem Provercited by 15
  5. 2018Acquisition of Phrase Correspondences using Natural Deduction Proofscited by 9
  6. 2015Representing Meaning with a Combination of Logical and Distributional Modelscited by 7
  7. 2020Learning as Abduction: Trainable Natural Logic Theorem Prover for Natural Language Inferencecited by 6
  8. 2021Monotonicity Marking from Universal Dependency Treescited by 3
  9. 2020Combining Event Semantics and Degree Semantics for Natural Language Inferencecited by 2
  10. 2017Task-Specific Attentive Pooling of Phrase Alignments Contributes to Sentence Matchingcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 4; 2022: 7; 2023: 3; 2024: 3; 2025: 6; 2026: 2.

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  1. 2022Generating Natural Language Proofs with Verifier-Guided Searchcited by 52
  2. 2022Is Neuro-Symbolic AI Meeting its Promise in Natural Language Processing? A Structured Reviewcited by 38
  3. 2022Natural Language Deduction through Search over Statement Compositionscited by 35
  4. 2022Logical Reasoning with Span-Level Predictions for Interpretable and Robust NLI Modelscited by 18
  5. 2023RECKONING: Reasoning through Dynamic Knowledge Encodingcited by 18
  6. 2025ARGUS: Hallucination and Omission Evaluation in Video-LLMscited by 16
  7. 2021Weakly Supervised Explainable Phrasal Reasoning with Neural Fuzzy Logiccited by 10
  8. 2022Neuro-symbolic Natural Logic with Introspective Revision for Natural Language Inferencecited by 9
  9. 2021PECO: Examining Single Sentence Label Leakage in Natural Language Inference Datasets through Progressive Evaluation of Cluster Outlierscited by 8
  10. 2024A Novel Cartography-Based Curriculum Learning Method Applied on RoNLI: The First Romanian Natural Language Inference Corpuscited by 8
  11. 2024Data-Efficient Learning with Neural Programscited by 7
  12. 2021Probing Linguistic Information For Logical Inference In Pre-trained Language Modelscited by 6
13 more
  1. 2025Unlocking the Potential of Generative AI through Neuro-Symbolic Architectures: Benefits and Limitationscited by 6
  2. 2024Dolphin: A Programmable Framework for Scalable Neurosymbolic Learningcited by 5
  3. 2021Decomposing Natural Logic Inferences in Neural NLIcited by 4
  4. 2025Lobster: A GPU-Accelerated Framework for Neurosymbolic Programmingcited by 4
  5. 2023NatLogAttack: A Framework for Attacking Natural Language Inference Models with Natural Logiccited by 3
  6. 2025Self-Rationalization in the Wild: A Large Scale Out-of-Distribution Evaluation on NLI-related taskscited by 1
  7. 2025When Does Meaning Backfire? Investigating the Role of AMRs in NLIcited by 1
  8. 2022An Understanding-Oriented Robust Machine Reading Comprehension Modelcited by 0
  9. 2022The Path to Autonomous Learnerscited by 0
  10. 2023Extracting Mathematical Concepts with Large Language Modelscited by 0
  11. 2025Quantum NLP models on Natural Language Inferencecited by 0
  12. 2026Explaining Textual Entailment with Lexical Entailments: Using LLMs to Supply Lexical Relations for Formal Proofscited by 0
  13. 2026Learning Proposition-Level Links from Sentence-Level Supervision for Neuro-Symbolic NLIcited by 0