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Recurrent Neural Network Grammars

arXiv 1602.07776 ↗First posted 25 Feb 2016cs.CL cs.NEcited by 109

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Abstract (arXiv)

We introduce recurrent neural network grammars, probabilistic models of sentences with explicit phrase structure. We explain efficient inference procedures that allow application to both parsing and language modeling. Experiments show that they provide better parsing in English than any single previously published supervised generative model and better language modeling than state-of-the-art sequential RNNs in English and Chinese.

Key facts. Cited by 109 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Feb 2016.

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Citing papers per year: 2021: 31; 2022: 23; 2023: 22; 2024: 15; 2025: 6; 2026: 12.

109 papers.

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  1. 2023Grammar-Constrained Decoding for Structured NLP Tasks without Finetuningcited by 186
  2. 2021Structured Prediction as Translation between Augmented Natural Languagescited by 183
  3. 2021How much do language models copy from their training data? Evaluating linguistic novelty in text generation using RAVENcited by 124
  4. 2022Why Does Surprisal From Larger Transformer-Based Language Models Provide a Poorer Fit to Human Reading Times?cited by 111
  5. 2021DeepStochLog: Neural Stochastic Logic Programmingcited by 58
  6. 2023Grammar Prompting for Domain-Specific Language Generation with Large Language Modelscited by 58
  7. 2024Towards a Foundation Model for Partial Differential Equations: Multi-Operator Learning and Extrapolationcited by 45
  8. 2022Autoregressive Structured Prediction with Language Modelscited by 43
  9. 2021Refining Targeted Syntactic Evaluation of Language Modelscited by 41
  10. 2022Can language models handle recursively nested grammatical structures? A case study on comparing models and humanscited by 41
  11. 2022Transformer Grammars: Augmenting Transformer Language Models with Syntactic Inductive Biases at Scalecited by 40
  12. 2022Characterizing Intrinsic Compositionality in Transformers with Tree Projectionscited by 40
38 more
  1. 2021A Targeted Assessment of Incremental Processing in Neural LanguageModels and Humanscited by 32
  2. 2021Sequence-to-Sequence Learning with Latent Neural Grammarscited by 30
  3. 2022Syntactic Surprisal From Neural Models Predicts, But Underestimates, Human Processing Difficulty From Syntactic Ambiguitiescited by 30
  4. 2025Large Language Models Are Human-Like Internallycited by 28
  5. 2021Structural Guidance for Transformer Language Modelscited by 25
  6. 2022Probing for Incremental Parse States in Autoregressive Language Modelscited by 24
  7. 2021Bottom-Up Constituency Parsing and Nested Named Entity Recognition with Pointer Networkscited by 21
  8. 2021R2D2: Recursive Transformer based on Differentiable Tree for Interpretable Hierarchical Language Modelingcited by 19
  9. 2023Pushdown Layers: Encoding Recursive Structure in Transformer Language Modelscited by 18
  10. 2023Stack Attention: Improving the Ability of Transformers to Model Hierarchical Patternscited by 17
  11. 2021Learning Hierarchical Structures with Differentiable Nondeterministic Stackscited by 16
  12. 2023PROSE: Predicting Operators and Symbolic Expressions using Multimodal Transformerscited by 16
  13. 2021Discovering Non-monotonic Autoregressive Orderings with Variational Inferencecited by 15
  14. 2023ROSE: A Neurocomputational Architecture for Syntaxcited by 15
  15. 2021Cost-effective End-to-end Information Extraction for Semi-structured Document Imagescited by 14
  16. 2022Fast-R2D2: A Pretrained Recursive Neural Network based on Pruned CKY for Grammar Induction and Text Representationcited by 14
  17. 2024LeMON: Learning to Learn Multi-Operator Networkscited by 13
  18. 2021Sequential Cross-Document Coreference Resolutioncited by 12
  19. 2021Effective Batching for Recurrent Neural Network Grammarscited by 12
  20. 2022Modeling structure-building in the brain with CCG parsing and large language modelscited by 11
  21. 2023Lewis's Signaling Game as beta-VAE For Natural Word Lengths and Segmentscited by 11
  22. 2024Emergent Word Order Universals from Cognitively-Motivated Language Modelscited by 11
  23. 2021Linguistic Frameworks Go Toe-to-Toe at Neuro-Symbolic Language Modelingcited by 10
  24. 2024Retrieval is Accurate Generationcited by 10
  25. 2022Lexical Generalization Improves with Larger Models and Longer Trainingcited by 9
  26. 2024Generative Pretrained Structured Transformers: Unsupervised Syntactic Language Models at Scalecited by 9
  27. 2021Semantic Parsing in Task-Oriented Dialog with Recursive Insertion-based Encodercited by 8
  28. 2023Explicit Syntactic Guidance for Neural Text Generationcited by 8
  29. 2021A Conditional Splitting Framework for Efficient Constituency Parsingcited by 7
  30. 2022Conditional set generation using Seq2seq modelscited by 7
  31. 2024Dependency Transformer Grammars: Integrating Dependency Structures into Transformer Language Modelscited by 7
  32. 2021Paired Examples as Indirect Supervision in Latent Decision Modelscited by 6
  33. 2021Schr"odinger's Tree -- On Syntax and Neural Language Modelscited by 6
  34. 2022Connecting Neural Response measurements & Computational Models of language: a non-comprehensive guidecited by 6
  35. 2023Ensemble Distillation for Unsupervised Constituency Parsingcited by 6
  36. 2023How Well Do Text Embedding Models Understand Syntax?cited by 6
  37. 2021The Limitations of Limited Context for Constituency Parsingcited by 5
  38. 2021Modeling Human Sentence Processing with Left-Corner Recurrent Neural Network Grammarscited by 5

50 most cited of 109 All 109 (JSON)