---
title: "Who cites The Penn Treebank: Annotating Predicate Argument…"
description: "The papers of the Elamur Research library that cite «The Penn Treebank: Annotating Predicate Argument Structure», found in their bibliographies."
canonical: "https://research.elamur.ai/free/en/papers/bank-1994-marcus-penn-treebank-annotating-predicate-argument/cited-by/"
---
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# Who cites «The Penn Treebank: Annotating Predicate Argument Structure»

314 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. [← the card](https://md.research.elamur.ai/free/en/papers/bank-1994-marcus-penn-treebank-annotating-predicate-argument/)

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1. [3BASiL: An Algorithmic Framework for Sparse plus Low-Rank Compression of LLMs](https://arxiv.org/abs/2603.01376) *— 2026 · arXiv:2603.01376*
2. [Detecting Potentially Under-annotated Explicit Discourse Connectives in the Penn Discourse Treebank (PDTB-3) with LLMs](https://aclanthology.org/2026.lrec-1.158/) *— 2026 · Proceedings of the Fifteenth Language Resources and Evaluation Conference*
3. [FAMPWQ: Fisher Information-based Adaptive Mixed Precision Weight Quantization for Effective LLM Inference](https://arxiv.org/abs/2608.24945) *— 2026 · arXiv:2608.24945*
4. [Implicit Representations of Grammaticality in Language Models](https://aclanthology.org/2026.acl-long.686/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
5. [LBLLM: Lightweight Binarization of Large Language Models via Three-Stage Distillation](https://aclanthology.org/2026.acl-long.1640/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
6. [Minimizing the Hidden Cost of Scales: Graph-Guided Ultra-Low-Bit Quantization for Large Language Models](https://arxiv.org/abs/2606.05429) *— 2026 · arXiv:2606.05429*
7. [Achieving binary weight and activation for LLMs using Post-Training Quantization](https://aclanthology.org/2025.findings-acl.459/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
8. [DartQuant: Efficient Rotational Distribution Calibration for LLM Quantization](https://arxiv.org/abs/2511.04063) *— 2025 · arXiv:2511.04063*
9. [End-to-End On-Device Quantization-Aware Training for LLMs at Inference Cost](https://arxiv.org/abs/2509.00031) *— 2025 · arXiv:2509.00031*
10. [FISTAPruner: Layer-wise Post-training Pruning for Large Language Models](https://aclanthology.org/2025.emnlp-main.1498/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
11. [FedSpaLLM: Federated Pruning of Large Language Models](https://aclanthology.org/2025.naacl-long.424/) *— 2025 · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
12. [GANQ: GPU-Adaptive Non-Uniform Quantization for Large Language Models](https://arxiv.org/abs/2501.12956) *— 2025 · arXiv:2501.12956*
13. [Glider: Global and Local Instruction-Driven Expert Router](https://aclanthology.org/2025.emnlp-main.319/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
14. [HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs](https://arxiv.org/abs/2502.00899) *— 2025 · arXiv:2502.00899*
15. [LLMs syntactically adapt their language use to their conversational partner](https://aclanthology.org/2025.acl-short.68/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
16. [Language Modeling Using Entanglement Enhanced Tensor Trains](https://aclanthology.org/2025.rocling-main.27/) *— 2025 · Proceedings of the 37th Conference on Computational Linguistics and Speech Processing (ROCLING 2025)*
17. [Merging Two Grammar Worlds: Exploring the Relationship between Universal Dependencies and Signal Temporal Logic](https://aclanthology.org/2025.findings-ijcnlp.116/) *— 2025 · Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics*
18. [PTQTP: Post-Training Quantization to Trit-Planes for Large Language Models](https://arxiv.org/abs/2509.16989) *— 2025 · arXiv:2509.16989*
19. [Probability Distribution Collapse: A Critical Bottleneck to Compact Unsupervised Neural Grammar Induction](https://aclanthology.org/2025.emnlp-main.1694/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
20. [QoS-Efficient Serving of Multiple Mixture-of-Expert LLMs Using Partial Runtime Reconfiguration](https://arxiv.org/abs/2505.06481) *— 2025 · arXiv:2505.06481*
21. [SASQ: Static Activation Scaling for Quantization-Aware Training in Large Language Models](https://arxiv.org/abs/2512.14481) *— 2025 · arXiv:2512.14481*
22. [TSENOR: Highly-Efficient Algorithm for Finding Transposable N:M Sparse Masks](https://arxiv.org/abs/2505.23949) *— 2025 · arXiv:2505.23949*
23. [A Comprehensive Evaluation of Quantization Strategies for Large Language Models](https://aclanthology.org/2024.findings-acl.726/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
24. [A Transformer with Stack Attention](https://aclanthology.org/2024.findings-naacl.269/) *— 2024 · Findings of the Association for Computational Linguistics: NAACL 2024*
25. [ALPS: Improved Optimization for Highly Sparse One-Shot Pruning for Large Language Models](https://arxiv.org/abs/2406.07831) *— 2024 · arXiv:2406.07831*
26. [ATQ: Activation Transformation forWeight-Activation Quantization of Large Language Models](https://aclanthology.org/2024.findings-emnlp.1001/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
27. [Adjudicating LLMs as PropBank Adjudicators](https://aclanthology.org/2024.dmr-1.12/) *— 2024 · Proceedings of the Fifth International Workshop on Designing Meaning Representations @ LREC-COLING 2024*
28. [Anda: Unlocking Efficient LLM Inference with a Variable-Length Grouped Activation Data Format](https://arxiv.org/abs/2411.15982) *— 2024 · arXiv:2411.15982*
29. [BESA: Pruning Large Language Models with Blockwise Parameter-Efficient Sparsity Allocation](https://arxiv.org/abs/2402.16880) *— 2024 · arXiv:2402.16880*
30. [BiLLM: Pushing the Limit of Post-Training Quantization for LLMs](https://arxiv.org/abs/2402.04291) *— 2024 · arXiv:2402.04291*
31. [Compensate Quantization Errors: Make Weights Hierarchical to Compensate Each Other](https://aclanthology.org/2024.findings-naacl.173/) *— 2024 · Findings of the Association for Computational Linguistics: NAACL 2024*
32. [Critical Size Hypothesis: How Model Hyperparameters Correlate with Its Linguistic Abilities](https://aclanthology.org/2024.clasp-1.1/) *— 2024 · Proceedings of the 2024 CLASP Conference on Multimodality and Interaction in Language Learning*
33. [Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research](https://aclanthology.org/2024.acl-long.840/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
34. [Gaining More Insight into Neural Semantic Parsing with Challenging Benchmarks](https://aclanthology.org/2024.dmr-1.17/) *— 2024 · Proceedings of the Fifth International Workshop on Designing Meaning Representations @ LREC-COLING 2024*
35. [LRQuant: Learnable and Robust Post-Training Quantization for Large Language Models](https://aclanthology.org/2024.acl-long.122/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
36. [Lateral Inversions, Word Form/Order, Unnamed Grammatical Entities and Ambiguities in the Constituency Parsing and Annotation of the Igala Syntax through the English Language](https://aclanthology.org/2024.rail-1.17/) *— 2024 · Proceedings of the Fifth Workshop on Resources for African Indigenous Languages @ LREC-COLING 2024*
37. [Mixture of Experts with Mixture of Precisions for Tuning Quality of Service](https://arxiv.org/abs/2407.14417) *— 2024 · arXiv:2407.14417*
38. [Rethinking Pruning Large Language Models: Benefits and Pitfalls of Reconstruction Error Minimization](https://aclanthology.org/2024.emnlp-main.68/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
39. [Structural Optimization Ambiguity and Simplicity Bias in Unsupervised Neural Grammar Induction](https://aclanthology.org/2024.findings-acl.898/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
40. [A Pilot Study on Dialogue-Level Dependency Parsing for Chinese](https://aclanthology.org/2023.findings-acl.607/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
41. [Atom: Low-bit Quantization for Efficient and Accurate LLM Serving](https://arxiv.org/abs/2310.19102) *— 2023 · MLSys 2024*
42. [Compress, Then Prompt: Improving Accuracy-Efficiency Trade-off of LLM Inference with Transferable Prompt](https://arxiv.org/abs/2305.11186) *— 2023 · arXiv:2305.11186*
43. [ED-Batch: Efficient Automatic Batching of Dynamic Neural Networks via Learned Finite State Machines](https://arxiv.org/abs/2302.03851) *— 2023 · arXiv:2302.03851*
44. [EasyQuant: An Efficient Data-free Quantization Algorithm for LLMs](https://aclanthology.org/2023.emnlp-main.565/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
45. [OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Models](https://arxiv.org/abs/2306.02272) *— 2023 · arXiv:2306.02272*
46. [OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models](https://arxiv.org/abs/2308.13137) *— 2023 · arXiv:2308.13137*
47. [QuantEase: Optimization-based Quantization for Language Models](https://arxiv.org/abs/2309.01885) *— 2023 · arXiv:2309.01885*
48. [SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression](https://arxiv.org/abs/2306.03078) *— 2023 · arXiv:2306.03078*
49. [SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot](https://arxiv.org/abs/2301.00774) *— 2023 · arXiv:2301.00774*
50. [Syntax-guided Neural Module Distillation to Probe Compositionality in Sentence Embeddings](https://aclanthology.org/2023.eacl-main.229/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
51. [Unified Syntactic Annotation of English in the CGEL Framework](https://aclanthology.org/2023.law-1.22/) *— 2023 · LAW-XVII 2023 (Linguistic Annotation Workshop, ACL)*
52. [Unified Syntactic Annotation of English in the CGEL Framework](https://md.research.elamur.ai/free/en/papers/bank-2023-reynolds-unified-syntactic-annotation-english-cgel/) *— our card · 2023 · Proceedings of the 17th Linguistic Annotation Workshop (LAW-XVII)*
53. [Verifying Annotation Agreement without Multiple Experts: A Case Study with Gujarati SNACS](https://aclanthology.org/2023.findings-acl.696/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
54. [A Cognitive Approach to Annotating Causal Constructions in a Cross-Genre Corpus](https://aclanthology.org/2022.law-1.18/) *— 2022 · Proceedings of the 16th Linguistic Annotation Workshop (LAW-XVI) within LREC2022*
55. [A Survey of Implicit Discourse Relation Recognition](https://arxiv.org/abs/2203.02982) *— 2022 · arXiv:2203.02982*
56. [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://arxiv.org/abs/2206.04615) *— 2022 · arXiv:2206.04615 · Transactions on Machine Learning Research, May/2022, https://openreview.net/forum?id=uyTL5Bvosj*
57. [Building Analyses from Syntactic Inference in Local Languages: An HPSG Grammar Inference System](https://nejlt.ep.liu.se/article/view/4017) *— 2022 · Northern European Journal of Language Technology*
58. [Dynamic Programming in Rank Space: Scaling Structured Inference with Low-Rank HMMs and PCFGs](https://aclanthology.org/2022.naacl-main.353/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
59. [GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers](https://arxiv.org/abs/2210.17323) *— 2022 · arXiv:2210.17323*
60. [Improving the Extraction of Supertags for Constituency Parsing with Linear Context-Free Rewriting Systems](https://aclanthology.org/2022.findings-emnlp.105/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
61. [MuCPAD: A Multi-Domain Chinese Predicate-Argument Dataset](https://aclanthology.org/2022.naacl-main.123/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
62. [PresiUniv at TSAR-2022 Shared Task: Generation and Ranking of Simplification Substitutes of Complex Words in Multiple Languages](https://aclanthology.org/2022.tsar-1.22/) *— 2022 · Proceedings of the Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022)*
63. [SHARP: Search-Based Adversarial Attack for Structured Prediction](https://aclanthology.org/2022.findings-naacl.71/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
64. [Square One Bias in NLP: Towards a Multi-Dimensional Exploration of the Research Manifold](https://aclanthology.org/2022.findings-acl.184/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
65. [Stars at Qur”an QA 2022: Building Automatic Extractive Question Answering Systems for the Holy Qur”an with Transformer Models and Releasing a New Dataset](https://aclanthology.org/2022.osact-1.18/) *— 2022 · Proceedinsg of the 5th Workshop on Open-Source Arabic Corpora and Processing Tools with Shared Tasks on Qur'an QA and Fine-Grained Hate Speech Detection*
66. [Using Roark-Hollingshead Distance to Probe BERT's Syntactic Competence](https://aclanthology.org/2022.blackboxnlp-1.27/) *— 2022 · Proceedings of the Fifth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP*
67. [An Empirical Study of Compound PCFGs](https://aclanthology.org/2021.adaptnlp-1.17/) *— 2021 · Proceedings of the Second Workshop on Domain Adaptation for NLP*
68. [Effects of Duration, Locality, and Surprisal in Speech Disfluency Prediction in English Spontaneous Speech](https://aclanthology.org/2021.scil-1.9/) *— 2021 · Proceedings of the Society for Computation in Linguistics 2021*
69. [Explicitly Modeling Syntax in Language Models with Incremental Parsing and a Dynamic Oracle](https://aclanthology.org/2021.naacl-main.132/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
70. [Exploring the Sensory Spaces of English Perceptual Verbs in Natural Language Data](https://arxiv.org/abs/2110.09721) *— 2021 · arXiv:2110.09721 · CEUR-WS.org 2021*
71. [Extraposed relative clauses in Role and Reference Grammar. An analysis using Tree Wrapping Grammars](https://jlm.ipipan.waw.pl/index.php/JLM/article/view/255) *— 2021 · Journal of Language Modelling*
72. [Linguistic Dependencies and Statistical Dependence](https://aclanthology.org/2021.emnlp-main.234/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
73. [Neural Bi-Lexicalized PCFG Induction](https://aclanthology.org/2021.acl-long.209/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
74. [Supertagging-based Parsing with Linear Context-free Rewriting Systems](https://aclanthology.org/2021.naacl-main.232/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
75. [Syntactic Perturbations Reveal Representational Correlates of Hierarchical Phrase Structure in Pretrained Language Models](https://aclanthology.org/2021.repl4nlp-1.27/) *— 2021 · Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021)*
76. [Unsupervised Neural Machine Translation with Universal Grammar](https://aclanthology.org/2021.emnlp-main.261/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
77. [A Computational-Graph Partitioning Method for Training Memory-Constrained DNNs](https://arxiv.org/abs/2008.08636) *— 2020 · arXiv:2008.08636*
78. [Aspects of Terminological and Named Entity Knowledge within Rule-Based Machine Translation Models for Under-Resourced Neural Machine Translation Scenarios](https://arxiv.org/abs/2009.13398) *— 2020 · arXiv:2009.13398*
79. [CCOHA: Clean Corpus of Historical American English](https://md.research.elamur.ai/free/en/papers/alatrash-2020-ccoha-clean-corpus-historical-american/) *— our card · 2020 · Proceedings of the Twelfth Language Resources and Evaluation Conference*
80. [Calibrating Structured Output Predictors for Natural Language Processing](https://aclanthology.org/2020.acl-main.188/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
81. [EmpLite: A Lightweight Sequence Labeling Model for Emphasis Selection of Short Texts](https://aclanthology.org/2020.icon-workshop.3/) *— 2020 · Proceedings of the Workshop on Joint NLP Modelling for Conversational AI @ ICON 2020*
82. [Estimating POS Annotation Consistency of Different Treebanks in a Language](https://md.research.elamur.ai/free/en/papers/aggarwal-2020-estimating-pos-annotation-consistency-different/) *— our card · 2020 · Proceedings of the 19th International Workshop on Treebanks and Linguistic Theories*
83. [FENAS: Flexible and Expressive Neural Architecture Search](https://aclanthology.org/2020.findings-emnlp.258/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
84. [Generalizing Natural Language Analysis through Span-relation Representations](https://aclanthology.org/2020.acl-main.192/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
85. [How LSTM Encodes Syntax: Exploring Context Vectors and Semi-Quantization on Natural Text](https://aclanthology.org/2020.coling-main.356/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
86. [Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165) *— 2020 · arXiv:2005.14165*
87. [Language-Independent Tokenisation Rivals Language-Specific Tokenisation for Word Similarity Prediction](https://aclanthology.org/2020.lrec-1.475/) *— 2020 · Proceedings of the Twelfth Language Resources and Evaluation Conference*
88. [NoEl: An Annotated Corpus for Noun Ellipsis in English](https://md.research.elamur.ai/free/en/papers/gram-2020-khullar-noel-annotated-corpus-noun-ellipsis/) *— our card · 2020 · Proceedings of the Twelfth Language Resources and Evaluation Conference*
89. [Semantic Role Labeling with Heterogeneous Syntactic Knowledge](https://aclanthology.org/2020.coling-main.266/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
90. [Text Segmentation by Cross Segment Attention](https://aclanthology.org/2020.emnlp-main.380/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
91. [Unsupervised Formal Grammar Induction with Confidence](https://aclanthology.org/2020.scil-1.46/) *— 2020 · Proceedings of the Society for Computation in Linguistics 2020*
92. [A Comparative Corpus Analysis of PP Ordering in English and Chinese](https://aclanthology.org/W19-7905/) *— 2019 · Proceedings of the First Workshop on Quantitative Syntax (Quasy, SyntaxFest 2019)*
93. [AutoShrink: A Topology-aware NAS for Discovering Efficient Neural Architecture](https://arxiv.org/abs/1911.09251) *— 2019 · arXiv:1911.09251*
94. [Bootstrapping UD treebanks for Delexicalized Parsing](https://aclanthology.org/W19-6102/) *— 2019 · Proceedings of the 22nd Nordic Conference on Computational Linguistics*
95. [Divide and Extract – Disentangling Clause Splitting and Proposition Extraction](https://aclanthology.org/R19-1047/) *— 2019 · Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)*
96. [Ellipsis in Chinese AMR Corpus](https://aclanthology.org/W19-3310/) *— 2019 · Proceedings of the First International Workshop on Designing Meaning Representations*
97. [Hardware-Guided Symbiotic Training for Compact, Accurate, yet Execution-Efficient LSTM](https://arxiv.org/abs/1901.10997) *— 2019 · arXiv:1901.10997*
98. [Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks](https://aclanthology.org/P19-1320/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
99. [Leveraging Rule-Based Machine Translation Knowledge for Under-Resourced Neural Machine Translation Models](https://md.research.elamur.ai/free/en/papers/torregrosa-2019-leveraging-rule-based-machine-translation/) *— our card · 2019 · Proceedings of Machine Translation Summit XVII: Translator, Project and User Tracks*
100. [Low Level Linguistic Controls for Style Transfer and Content Preservation](https://aclanthology.org/W19-8628/) *— 2019 · Proceedings of the 12th International Conference on Natural Language Generation*

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