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# Who cites «The CommitmentBank: Investigating projection in naturally occurring discourse» · page 2

234 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. [← the card](https://md.mova.elamur.ai/free/en/docs/papers/enprag-2019-marneffe-commitmentbank-investigating-projection-naturally/)

Pages: [1](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/enprag-2019-marneffe-commitmentbank-investigating-projection-naturally/) · **2** · [3](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/enprag-2019-marneffe-commitmentbank-investigating-projection-naturally-3/)

1. [Trained on 100 million words and still in shape: BERT meets British National Corpus](https://aclanthology.org/2023.findings-eacl.146/) *— 2023 · Findings of the Association for Computational Linguistics: EACL 2023*
2. [Tree Prompting: Efficient Task Adaptation without Fine-Tuning](https://aclanthology.org/2023.emnlp-main.384/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
3. [Understanding the Role of Input Token Characters in Language Models: How Does Information Loss Affect Performance?](https://aclanthology.org/2023.emnlp-main.563/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
4. [When Your Language Model Cannot Even Do Determiners Right: Probing for Anti-Presuppositions and the Maximize Presupposition! Principle](https://md.mova.elamur.ai/free/en/docs/papers/sieker-2023-when-your-language-model-cannot/) *— our card · 2023 · Proceedings of the 6th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP*
5. [Where to start? Analyzing the potential value of intermediate models](https://aclanthology.org/2023.emnlp-main.90/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
6. [Why Can GPT Learn In-Context? Language Models Secretly Perform Gradient Descent as Meta-Optimizers](https://aclanthology.org/2023.findings-acl.247/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
7. [You Are What You Annotate: Towards Better Models through Annotator Representations](https://aclanthology.org/2023.findings-emnlp.832/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
8. [Zemi: Learning Zero-Shot Semi-Parametric Language Models from Multiple Tasks](https://aclanthology.org/2023.findings-acl.246/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
9. [A Pragmatics-Centered Evaluation Framework for Natural Language Understanding](https://aclanthology.org/2022.lrec-1.255/) *— 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference*
10. [Analyzing the Effects of Annotator Gender across NLP Tasks](https://aclanthology.org/2022.nlperspectives-1.2/) *— 2022 · Proceedings of the 1st Workshop on Perspectivist Approaches to NLP @LREC2022*
11. [Ask Me Anything: A simple strategy for prompting language models](https://arxiv.org/abs/2210.02441) *— 2022 · arXiv:2210.02441*
12. [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*
13. [Coherence boosting: When your pretrained language model is not paying enough attention](https://aclanthology.org/2022.acl-long.565/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
14. [Eliciting and Understanding Cross-task Skills with Task-level Mixture-of-Experts](https://aclanthology.org/2022.findings-emnlp.189/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
15. [Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity](https://aclanthology.org/2022.acl-long.556/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
16. [Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning](https://arxiv.org/abs/2205.05638) *— 2022 · arXiv:2205.05638*
17. [FewNLU: Benchmarking State-of-the-Art Methods for Few-Shot Natural Language Understanding](https://aclanthology.org/2022.acl-long.38/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
18. [FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning](https://aclanthology.org/2022.acl-long.592/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
19. [Foundation Models of Scientific Knowledge for Chemistry: Opportunities, Challenges and Lessons Learned](https://aclanthology.org/2022.bigscience-1.12/) *— 2022 · Proceedings of BigScience Episode \#5 – Workshop on Challenges & Perspectives in Creating Large Language Models*
20. [Ground-Truth Labels Matter: A Deeper Look into Input-Label Demonstrations](https://aclanthology.org/2022.emnlp-main.155/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
21. [Improving Sharpness-Aware Minimization with Fisher Mask for Better Generalization on Language Models](https://aclanthology.org/2022.findings-emnlp.300/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
22. [Improving the Sample Efficiency of Prompt Tuning with Domain Adaptation](https://aclanthology.org/2022.findings-emnlp.258/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
23. [Investigating the Performance of Transformer-Based NLI Models on Presuppositional Inferences](https://aclanthology.org/2022.coling-1.65/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
24. [Learning Instructions with Unlabeled Data for Zero-Shot Cross-Task Generalization](https://aclanthology.org/2022.emnlp-main.105/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
25. [Model Cascading: Towards Jointly Improving Efficiency and Accuracy of NLP Systems](https://aclanthology.org/2022.emnlp-main.756/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
26. [Nearest Neighbor Zero-Shot Inference](https://aclanthology.org/2022.emnlp-main.214/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
27. [On Measuring the Intrinsic Few-Shot Hardness of Datasets](https://aclanthology.org/2022.emnlp-main.262/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
28. [PPT: Pre-trained Prompt Tuning for Few-shot Learning](https://aclanthology.org/2022.acl-long.576/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
29. [Probing as Quantifying Inductive Bias](https://aclanthology.org/2022.acl-long.129/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
30. [Prompt Consistency for Zero-Shot Task Generalization](https://aclanthology.org/2022.findings-emnlp.192/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
31. [Prompt-free and Efficient Few-shot Learning with Language Models](https://aclanthology.org/2022.acl-long.254/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
32. [Re-Examining FactBank: Predicting the Author''s Presentation of Factuality](https://aclanthology.org/2022.coling-1.66/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
33. [Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://aclanthology.org/2022.emnlp-main.759/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
34. [Revisiting Parameter-Efficient Tuning: Are We Really There Yet?](https://aclanthology.org/2022.emnlp-main.168/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
35. [SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer](https://aclanthology.org/2022.acl-long.346/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
36. [ST-MoE: Designing Stable and Transferable Sparse Expert Models](https://arxiv.org/abs/2202.08906) *— 2022 · arXiv:2202.08906*
37. [Task Compass: Scaling Multi-task Pre-training with Task Prefix](https://aclanthology.org/2022.findings-emnlp.416/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
38. [The ``Problem'''' of Human Label Variation: On Ground Truth in Data, Modeling and Evaluation](https://aclanthology.org/2022.emnlp-main.731/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
39. [Tiny-Attention Adapter: Contexts Are More Important Than the Number of Parameters](https://aclanthology.org/2022.emnlp-main.444/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
40. [Transferring Confluent Knowledge to Argument Mining](https://aclanthology.org/2022.coling-1.597/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
41. [Vector-Quantized Input-Contextualized Soft Prompts for Natural Language Understanding](https://aclanthology.org/2022.emnlp-main.455/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
42. [Word Order Does Matter and Shuffled Language Models Know It](https://aclanthology.org/2022.acl-long.476/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
43. [Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice Perspective](https://aclanthology.org/2022.emnlp-main.474/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
44. [Beyond Reptile: Meta-Learned Dot-Product Maximization between Gradients for Improved Single-Task Regularization](https://aclanthology.org/2021.findings-emnlp.37/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
45. [Calibrate Before Use: Improving Few-Shot Performance of Language Models](https://arxiv.org/abs/2102.09690) *— 2021 · arXiv:2102.09690*
46. [Comparing Test Sets with Item Response Theory](https://aclanthology.org/2021.acl-long.92/) *— 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)*
47. [CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP](https://arxiv.org/abs/2104.08835) *— 2021 · arXiv:2104.08835*
48. [ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation](https://arxiv.org/abs/2107.02137) *— 2021 · arXiv:2107.02137*
49. [ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning](https://arxiv.org/abs/2111.10952) *— 2021 · arXiv:2111.10952*
50. [Finetuned Language Models Are Zero-Shot Learners](https://arxiv.org/abs/2109.01652) *— 2021 · arXiv:2109.01652*
51. [GLaM: Efficient Scaling of Language Models with Mixture-of-Experts](https://arxiv.org/abs/2112.06905) *— 2021 · arXiv:2112.06905*
52. [GPT Understands, Too](https://arxiv.org/abs/2103.10385) *— 2021 · arXiv:2103.10385*
53. [He Thinks He Knows Better than the Doctors: BERT for Event Factuality Fails on Pragmatics](https://md.mova.elamur.ai/free/en/docs/papers/jiang-2021-he-thinks-he-knows-better/) *— our card · 2021*
54. [How many data points is a prompt worth?](https://aclanthology.org/2021.naacl-main.208/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
55. [Identifying inherent disagreement in natural language inference](https://aclanthology.org/2021.naacl-main.390/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
56. [Issues with Entailment-based Zero-shot Text Classification](https://aclanthology.org/2021.acl-short.99/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)*
57. [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*
58. [Learn Continually, Generalize Rapidly: Lifelong Knowledge Accumulation for Few-shot Learning](https://aclanthology.org/2021.findings-emnlp.62/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
59. [MetaICL: Learning to Learn In Context](https://arxiv.org/abs/2110.15943) *— 2021 · arXiv:2110.15943*
60. [NOPE: A Corpus of Naturally-Occurring Presuppositions in English](https://md.mova.elamur.ai/free/en/docs/papers/parrish-2021-nope-corpus-naturally-occurring-presuppositions/) *— our card · 2021 · Proceedings of the 25th Conference on Computational Natural Language Learning*
61. [Surface Form Competition: Why the Highest Probability Answer Isn''t Always Right](https://aclanthology.org/2021.emnlp-main.564/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
62. [The Power of Scale for Parameter-Efficient Prompt Tuning](https://aclanthology.org/2021.emnlp-main.243/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
63. [True Few-Shot Learning with Language Models](https://arxiv.org/abs/2105.11447) *— 2021 · arXiv:2105.11447*
64. [UNICORN on RAINBOW: A Universal Commonsense Reasoning Model on a New Multitask Benchmark](https://arxiv.org/abs/2103.13009) *— 2021 · arXiv:2103.13009*
65. [WARP: Word-level Adversarial ReProgramming](https://arxiv.org/abs/2101.00121) *— 2021 · arXiv:2101.00121*
66. [Want To Reduce Labeling Cost? GPT-3 Can Help](https://aclanthology.org/2021.findings-emnlp.354/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
67. [AdapterFusion: Non-Destructive Task Composition for Transfer Learning](https://arxiv.org/abs/2005.00247) *— 2020 · arXiv:2005.00247 · Proceedings of EACL 2021*
68. [Are Natural Language Inference Models IMPPRESsive? Learning IMPlicature and PRESupposition](https://md.mova.elamur.ai/free/en/docs/papers/jeretic-2020-are-natural-language-inference-models/) *— our card · 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
69. [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) *— 2020 · arXiv:2006.03654*
70. [Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work?](https://aclanthology.org/2020.acl-main.467/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
71. [Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165) *— 2020 · arXiv:2005.14165*
72. [New Protocols and Negative Results for Textual Entailment Data Collection](https://aclanthology.org/2020.emnlp-main.658/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
73. [Which *BERT? A Survey Organizing Contextualized Encoders](https://aclanthology.org/2020.emnlp-main.608/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
74. [Do You Know That Florence Is Packed with Visitors? Evaluating State-of-the-art Models of Speaker Commitment](https://aclanthology.org/P19-1412/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
75. [Evaluating BERT for natural language inference: A case study on the CommitmentBank](https://aclanthology.org/D19-1630/) *— 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. [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) *— 2019 · arXiv:1910.10683*
77. [Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches](https://arxiv.org/abs/1904.01172) *— 2019 · arXiv:1904.01172*
78. [SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems](https://arxiv.org/abs/1905.00537) *— 2019 · arXiv:1905.00537*
79. azin-2026-do-language-models-know-theo
80. belanec-2026-peft-bench-parameter-efficient-fine
81. belanec-2026-peft-factory-unified-parameter-efficient
82. bhardwaj-2023-knn-cm-non-parametric-inference
83. feng-2023-learning-predict-task-transferability-soft
84. geng-2025-great-memory-shallow-reasoning-limits
85. gong-2023-improving-input-label-mapping-demonstration
86. gozzi-2025-bidirectional-emotional-influence-human-llm
87. groeneveld-2023-catwalk-unified-language-model-evaluation
88. ha-2023-two-examples-are-better-than
89. hejabi-2026-flip-flop-consistency-unsupervised-training
90. khanmohammadi-2025-calibrating-llm-confidence-probing-perturbed
91. kim-2025-first-attentions-last-better-exploiting
92. kuang-2024-multi-scale-prompt-memory-augmented
93. l-2024-hire-high-recall-approximate-top
94. lee-2023-bayesian-multi-task-transfer-learning
95. li-2023-revisiting-k-nn-fine-tuning
96. li-2024-feature-adaptive-data-scalable-context
97. li-2024-lorasc-expressive-generalizable-low-rank
98. li-2025-glider-global-local-instruction-driven
99. li-2025-task-calibration-calibrating-large-language
100. lin-2024-accept-adaptive-codebook-composite-efficient

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