---
title: "Who cites Effective Self-Training for Parsing"
description: "The papers of the Elamur Research library that cite «Effective Self-Training for Parsing», found in their bibliographies."
canonical: "https://research.elamur.ai/free/en/papers/aclbulk-2006-mcclosky-effective-self-training-parsing/cited-by/"
---
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# Who cites «Effective Self-Training for Parsing»

252 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/aclbulk-2006-mcclosky-effective-self-training-parsing/)

Pages: **1** · [2](https://md.research.elamur.ai/free/en/papers/aclbulk-2006-mcclosky-effective-self-training-parsing/cited-by/page/2/) · [3](https://md.research.elamur.ai/free/en/papers/aclbulk-2006-mcclosky-effective-self-training-parsing/cited-by/page/3/)

1. [Creating a High Quality Abstract Meaning Representation Dataset Automatically](https://aclanthology.org/2026.lrec-1.932/) *— 2026 · Proceedings of the Fifteenth Language Resources and Evaluation Conference*
2. [Discovering and Causally Validating Emotion-Sensitive Neurons in Large Audio-Language Models](https://aclanthology.org/2026.acl-long.687/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
3. [Contrastive Learning on LLM Back Generation Treebank for Cross-domain Constituency Parsing](https://aclanthology.org/2025.acl-long.1331/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
4. [DreamSync: Aligning Text-to-Image Generation with Image Understanding Feedback](https://aclanthology.org/2025.naacl-long.304/) *— 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)*
5. [Leveraging the Structure of Pre-trained Embeddings to Minimize Annotation Effort](https://aclanthology.org/2024.naacl-long.387/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
6. [Multistage Collaborative Knowledge Distillation from a Large Language Model for Semi-Supervised Sequence Generation](https://aclanthology.org/2024.acl-long.766/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
7. [Source-Free Domain Adaptation for Question Answering with Masked Self-training](https://aclanthology.org/2024.tacl-1.40/) *— 2024 · Transactions of the Association for Computational Linguistics*
8. [Structured Tree Alignment for Evaluation of (Speech) Constituency Parsing](https://aclanthology.org/2024.acl-long.666/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
9. [Data-efficient Active Learning for Structured Prediction with Partial Annotation and Self-Training](https://aclanthology.org/2023.findings-emnlp.865/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
10. [Friend-training: Learning from Models of Different but Related Tasks](https://aclanthology.org/2023.eacl-main.18/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
11. [Learning from a Friend: Improving Event Extraction via Self-Training with Feedback from Abstract Meaning Representation](https://aclanthology.org/2023.findings-acl.662/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
12. [Reducing cohort bias in natural language understanding systems with targeted self-training scheme](https://aclanthology.org/2023.acl-industry.53/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track)*
13. [Rethinking Semi-supervised Learning with Language Models](https://aclanthology.org/2023.findings-acl.347/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
14. [SCENE: Self-Labeled Counterfactuals for Extrapolating to Negative Examples](https://aclanthology.org/2023.emnlp-main.485/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
15. [Toolformer: Language Models Can Teach Themselves to Use Tools](https://arxiv.org/abs/2302.04761) *— 2023 · arXiv:2302.04761*
16. [A Comparison of Strategies for Source-Free Domain Adaptation](https://aclanthology.org/2022.acl-long.572/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
17. [A Few-Shot Semantic Parser for Wizard-of-Oz Dialogues with the Precise ThingTalk Representation](https://aclanthology.org/2022.findings-acl.317/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
18. [Adapting to the Long Tail: A Meta-Analysis of Transfer Learning Research for Language Understanding Tasks](https://aclanthology.org/2022.tacl-1.56/) *— 2022 · Transactions of the Association for Computational Linguistics*
19. [Automatic Rule Induction for Efficient Semi-Supervised Learning](https://aclanthology.org/2022.findings-emnlp.3/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
20. [Co-training an Unsupervised Constituency Parser with Weak Supervision](https://aclanthology.org/2022.findings-acl.101/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
21. [Coordination Generation via Synchronized Text-Infilling](https://aclanthology.org/2022.coling-1.517/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
22. [Improving Code-Switching Dependency Parsing with Semi-Supervised Auxiliary Tasks](https://aclanthology.org/2022.findings-naacl.87/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
23. [Improving Low-resource RRG Parsing with Cross-lingual Self-training](https://aclanthology.org/2022.coling-1.384/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
24. [Multi-Domain Targeted Sentiment Analysis](https://aclanthology.org/2022.naacl-main.198/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
25. [Neural Generation Meets Real People: Building a Social, Informative Open-Domain Dialogue Agent](https://aclanthology.org/2022.sigdial-1.37/) *— 2022 · Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue*
26. [Out-of-Domain Discourse Dependency Parsing via Bootstrapping: An Empirical Analysis on Its Effectiveness and Limitation](https://aclanthology.org/2022.tacl-1.8/) *— 2022 · Transactions of the Association for Computational Linguistics*
27. [STAD: Self-Training with Ambiguous Data for Low-Resource Relation Extraction](https://aclanthology.org/2022.coling-1.178/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
28. [Semi-supervised Domain Adaptation for Dependency Parsing with Dynamic Matching Network](https://aclanthology.org/2022.acl-long.74/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
29. [SynGEC: Syntax-Enhanced Grammatical Error Correction with a Tailored GEC-Oriented Parser](https://aclanthology.org/2022.emnlp-main.162/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
30. [Thematic Fit Bits: Annotation Quality and Quantity Interplay for Event Participant Representation](https://aclanthology.org/2022.lrec-1.556/) *— 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference*
31. [Unsupervised Cross-Lingual Transfer of Structured Predictors without Source Data](https://aclanthology.org/2022.naacl-main.149/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
32. [Unsupervised Domain Adaptation for Question Generation with DomainData Selection and Self-training](https://aclanthology.org/2022.findings-naacl.183/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
33. [Break-It-Fix-It: Unsupervised Learning for Program Repair](https://arxiv.org/abs/2106.06600) *— 2021 · arXiv:2106.06600*
34. [Capturing document context inside sentence-level neural machine translation models with self-training](https://aclanthology.org/2021.codi-main.14/) *— 2021 · Proceedings of the 2nd Workshop on Computational Approaches to Discourse*
35. [Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond](https://arxiv.org/abs/2109.00725) *— 2021 · arXiv:2109.00725*
36. [Combining Deep Generative Models and Multi-lingual Pretraining for Semi-supervised Document Classification](https://aclanthology.org/2021.eacl-main.76/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
37. [Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference](https://aclanthology.org/2021.eacl-main.20/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
38. [Improving Neural RST Parsing Model with Silver Agreement Subtrees](https://aclanthology.org/2021.naacl-main.127/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
39. [It”s Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners](https://aclanthology.org/2021.naacl-main.185/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
40. [Multi-View Cross-Lingual Structured Prediction with Minimum Supervision](https://aclanthology.org/2021.acl-long.207/) *— 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)*
41. [Multilingual and Multilabel Emotion Recognition using Virtual Adversarial Training](https://aclanthology.org/2021.mrl-1.7/) *— 2021 · Proceedings of the 1st Workshop on Multilingual Representation Learning*
42. [Revisiting Self-training for Few-shot Learning of Language Model](https://aclanthology.org/2021.emnlp-main.718/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
43. [Revisiting Tri-training of Dependency Parsers](https://aclanthology.org/2021.emnlp-main.745/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
44. [STraTA: Self-Training with Task Augmentation for Better Few-shot Learning](https://aclanthology.org/2021.emnlp-main.462/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
45. [Self-Training for Compositional Neural NLG in Task-Oriented Dialogue](https://aclanthology.org/2021.inlg-1.10/) *— 2021 · Proceedings of the 14th International Conference on Natural Language Generation*
46. [Self-Training for Unsupervised Neural Machine Translation in Unbalanced Training Data Scenarios](https://aclanthology.org/2021.naacl-main.311/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
47. [Self-training Improves Pre-training for Natural Language Understanding](https://aclanthology.org/2021.naacl-main.426/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
48. [A Semi-supervised Approach for De-identification of Swedish Clinical Text](https://aclanthology.org/2020.lrec-1.547/) *— 2020 · Proceedings of the Twelfth Language Resources and Evaluation Conference*
49. [Affective Event Classification with Discourse-enhanced Self-training](https://aclanthology.org/2020.emnlp-main.452/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
50. [An Imitation Game for Learning Semantic Parsers from User Interaction](https://aclanthology.org/2020.emnlp-main.559/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
51. [AutoQA: From Databases To QA Semantic Parsers With Only Synthetic Training Data](https://aclanthology.org/2020.emnlp-main.31/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
52. [Combining Self-Training and Self-Supervised Learning for Unsupervised Disfluency Detection](https://aclanthology.org/2020.emnlp-main.142/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
53. [Cross-Lingual Dependency Parsing via Self-Training](https://aclanthology.org/2020.ccl-1.75/) *— 2020 · Proceedings of the 19th Chinese National Conference on Computational Linguistics*
54. [Data Augmentation via Subtree Swapping for Dependency Parsing of Low-Resource Languages](https://aclanthology.org/2020.coling-main.339/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
55. [Detecting Urgency Status of Crisis Tweets: A Transfer Learning Approach for Low Resource Languages](https://aclanthology.org/2020.coling-main.414/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
56. [Improving Disfluency Detection by Self-Training a Self-Attentive Model](https://aclanthology.org/2020.acl-main.346/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
57. [Leveraging User Paraphrasing Behavior In Dialog Systems To Automatically Collect Annotations For Long-Tail Utterances](https://aclanthology.org/2020.coling-industry.3/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics: Industry Track*
58. [On the Role of Supervision in Unsupervised Constituency Parsing](https://aclanthology.org/2020.emnlp-main.614/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
59. [Self-Training for Unsupervised Parsing with PRPN](https://aclanthology.org/2020.iwpt-1.11/) *— 2020 · Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies*
60. [Semi-Supervised Dependency Parsing with Arc-Factored Variational Autoencoding](https://aclanthology.org/2020.coling-main.224/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
61. [Semi-Supervised Tri-Training for Explicit Discourse Argument Expansion](https://aclanthology.org/2020.lrec-1.139/) *— 2020 · Proceedings of the Twelfth Language Resources and Evaluation Conference*
62. [Semi-supervised Domain Adaptation for Dependency Parsing via Improved Contextualized Word Representations](https://aclanthology.org/2020.coling-main.338/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
63. [SeqVAT: Virtual Adversarial Training for Semi-Supervised Sequence Labeling](https://aclanthology.org/2020.acl-main.777/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
64. [To BERT or Not to BERT: Comparing Task-specific and Task-agnostic Semi-Supervised Approaches for Sequence Tagging](https://aclanthology.org/2020.emnlp-main.636/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
65. [Unsupervised Discourse Constituency Parsing Using Viterbi EM](https://aclanthology.org/2020.tacl-1.15/) *— 2020 · Transactions of the Association for Computational Linguistics*
66. [Zero-shot Text Classification via Reinforced Self-training](https://aclanthology.org/2020.acl-main.272/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
67. [A Robust Self-Learning Framework for Cross-Lingual Text Classification](https://aclanthology.org/D19-1658/) *— 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)*
68. [Adversarial Learning with Contextual Embeddings for Zero-resource Cross-lingual Classification and NER](https://aclanthology.org/D19-1138/) *— 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)*
69. [Bilingual Low-Resource Neural Machine Translation with Round-Tripping: The Case of Persian-Spanish](https://aclanthology.org/R19-1003/) *— 2019 · Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)*
70. [Cross-lingual Annotation Projection Is Effective for Neural Part-of-Speech Tagging](https://aclanthology.org/W19-1425/) *— 2019 · Proceedings of the Sixth Workshop on NLP for Similar Languages, Varieties and Dialects*
71. [Leveraging Just a Few Keywords for Fine-Grained Aspect Detection Through Weakly Supervised Co-Training](https://aclanthology.org/D19-1468/) *— 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)*
72. [Neural Dependency Parsing of Biomedical Text: TurkuNLP entry in the CRAFT Structural Annotation Task](https://aclanthology.org/D19-5728/) *— 2019 · Proceedings of the 5th Workshop on BioNLP Open Shared Tasks*
73. [Neural Semantic Parsing in Low-Resource Settings with Back-Translation and Meta-Learning](https://arxiv.org/abs/1909.05438) *— 2019 · arXiv:1909.05438*
74. [Paraphrase Generation for Semi-Supervised Learning in NLU](https://aclanthology.org/W19-2306/) *— 2019 · Proceedings of the Workshop on Methods for Optimizing and Evaluating Neural Language Generation*
75. [Text Genre and Training Data Size in Human-like Parsing](https://aclanthology.org/D19-1594/) *— 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)*
76. [To Annotate or Not? Predicting Performance Drop under Domain Shift](https://aclanthology.org/D19-1222/) *— 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)*
77. [Variational Pretraining for Semi-supervised Text Classification](https://aclanthology.org/P19-1590/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
78. [Extending a Parser to Distant Domains Using a Few Dozen Partially Annotated Examples](https://md.research.elamur.ai/free/en/papers/acl-2018-joshi-extending-parser-distant-domains-few/) *— our card · 2018 · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
79. [Human Needs Categorization of Affective Events Using Labeled and Unlabeled Data](https://aclanthology.org/N18-1174/) *— 2018 · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)*
80. [Improving the results of string kernels in sentiment analysis and Arabic dialect identification by adapting them to your test set](https://aclanthology.org/D18-1135/) *— 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing*
81. [Le benchmarking de la reconnaissance d'entités nommées pour le français (Benchmarking for French NER)](https://aclanthology.org/2018.jeptalnrecital-court.6/) *— 2018 · Actes de la Conférence TALN. Volume 1 - Articles longs, articles courts de TALN*
82. [Semi-Supervised Sequence Modeling with Cross-View Training](https://aclanthology.org/D18-1217/) *— 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing*
83. [Speaker-Follower Models for Vision-and-Language Navigation](https://arxiv.org/abs/1806.02724) *— 2018 · arXiv:1806.02724*
84. [Strong Baselines for Neural Semi-Supervised Learning under Domain Shift](https://aclanthology.org/P18-1096/) *— 2018 · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
85. [Wronging a Right: Generating Better Errors to Improve Grammatical Error Detection](https://aclanthology.org/D18-1541/) *— 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing*
86. [Attention Is All You Need](https://arxiv.org/abs/1706.03762) *— 2017 · arXiv:1706.03762*
87. [Semi-supervised Structured Prediction with Neural CRF Autoencoder](https://aclanthology.org/D17-1179/) *— 2017 · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing*
88. [Shift-Reduce Constituent Parsing with Neural Lookahead Features](https://aclanthology.org/Q17-1004/) *— 2017 · Transactions of the Association for Computational Linguistics*
89. [Skip-Prop: Representing Sentences with One Vector Per Proposition](https://aclanthology.org/W17-6936/) *— 2017 · Proceedings of the 12th International Conference on Computational Semantics (IWCS) –- Short papers*
90. [Three-phase training to address data sparsity in Neural Machine Translation](https://aclanthology.org/W17-7503/) *— 2017 · Proceedings of the 14th International Conference on Natural Language Processing (ICON-2017)*
91. [Transferring Semantic Roles Using Translation and Syntactic Information](https://aclanthology.org/I17-2003/) *— 2017 · Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)*
92. [Unsupervised Domain Adaptation for Clinical Negation Detection](https://aclanthology.org/W17-2320/) *— 2017 · Proceedings of the 16th BioNLP Workshop*
93. [Word-Context Character Embeddings for Chinese Word Segmentation](https://aclanthology.org/D17-1079/) *— 2017 · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing*
94. [Addressing Limited Data for Textual Entailment Across Domains](https://aclanthology.org/P16-1118/) *— 2016 · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
95. [Decomposing Bilexical Dependencies into Semantic and Syntactic Vectors](https://aclanthology.org/W16-1615/) *— 2016 · Proceedings of the 1st Workshop on Representation Learning for NLP*
96. [Embedding Lexical Features via Low-Rank Tensors](https://aclanthology.org/N16-1117/) *— 2016 · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
97. [Evaluating a Deterministic Shift-Reduce Neural Parser for Constituent Parsing](https://aclanthology.org/L16-1104/) *— 2016 · Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)*
98. [From Noisy Questions to Minecraft Texts: Annotation Challenges in Extreme Syntax Scenario](https://aclanthology.org/W16-3905/) *— 2016 · Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)*
99. [ICL-HD at SemEval-2016 Task 8: Meaning Representation Parsing - Augmenting AMR Parsing with a Preposition Semantic Role Labeling Neural Network](https://aclanthology.org/S16-1179/) *— 2016 · Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)*
100. [Improving Neural Machine Translation Models with Monolingual Data](https://aclanthology.org/P16-1009/) *— 2016 · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*

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