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# Who cites «Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference» · page 4

699 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/mccoy-2019-right-wrong-reasons-diagnosing-syntactic/)

Pages: [1](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/mccoy-2019-right-wrong-reasons-diagnosing-syntactic/) · [2](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/mccoy-2019-right-wrong-reasons-diagnosing-syntactic-2/) · [3](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/mccoy-2019-right-wrong-reasons-diagnosing-syntactic-3/) · **4** · [5](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/mccoy-2019-right-wrong-reasons-diagnosing-syntactic-5/) · [6](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/mccoy-2019-right-wrong-reasons-diagnosing-syntactic-6/) · [7](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/mccoy-2019-right-wrong-reasons-diagnosing-syntactic-7/)

1. [Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model](https://arxiv.org/abs/2201.11990) *— 2022 · arXiv:2201.11990*
2. [VALUE: Understanding Dialect Disparity in NLU](https://md.mova.elamur.ai/free/en/docs/papers/ziems-2022-value-understanding-dialect-disparity-nlu/) *— our card · 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
3. [ViNLI: A Vietnamese Corpus for Studies on Open-Domain Natural Language Inference](https://aclanthology.org/2022.coling-1.339/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
4. [WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation](https://arxiv.org/abs/2201.05955) *— 2022 · arXiv:2201.05955*
5. [When Chosen Wisely, More Data Is What You Need: A Universal Sample-Efficient Strategy For Data Augmentation](https://aclanthology.org/2022.findings-acl.84/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
6. [When Does Syntax Mediate Neural Language Model Performance? Evidence from Dropout Probes](https://aclanthology.org/2022.naacl-main.394/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
7. [Why is Winoground Hard? Investigating Failures in Visuolinguistic Compositionality](https://aclanthology.org/2022.emnlp-main.143/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
8. [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)*
9. [Your fairness may vary: Pretrained language model fairness in toxic text classification](https://aclanthology.org/2022.findings-acl.176/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
10. [``No, They Did Not'''': Dialogue Response Dynamics in Pre-trained Language Models](https://aclanthology.org/2022.coling-1.72/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
11. [``Will You Find These Shortcuts?'''' A Protocol for Evaluating the Faithfulness of Input Salience Methods for Text Classification](https://aclanthology.org/2022.emnlp-main.64/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
12. [e-CARE: a New Dataset for Exploring Explainable Causal Reasoning](https://aclanthology.org/2022.acl-long.33/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
13. [A Deep Metric Learning Approach to Account Linking](https://aclanthology.org/2021.naacl-main.415/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
14. [A Logic-Based Framework for Natural Language Inference in Dutch](https://arxiv.org/abs/2110.03323) *— 2021 · arXiv:2110.03323*
15. [A Multilingual Benchmark for Probing Negation-Awareness with Minimal Pairs](https://aclanthology.org/2021.conll-1.19/) *— 2021 · Proceedings of the 25th Conference on Computational Natural Language Learning*
16. [AESOP: Paraphrase Generation with Adaptive Syntactic Control](https://aclanthology.org/2021.emnlp-main.420/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
17. [Abstraction and analogy-making in artificial intelligence](https://arxiv.org/abs/2102.10717) *— 2021*
18. [Alignment Rationale for Natural Language Inference](https://aclanthology.org/2021.acl-long.417/) *— 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)*
19. [An Empirical Comparison of Instance Attribution Methods for NLP](https://aclanthology.org/2021.naacl-main.75/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
20. [Annotation Difficulties in Natural Language Inference](https://aclanthology.org/2021.stil-1.27/) *— 2021 · Proceedings of the 13th Brazilian Symposium in Information and Human Language Technology*
21. [Are Larger Pretrained Language Models Uniformly Better? Comparing Performance at the Instance Level](https://aclanthology.org/2021.findings-acl.334/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
22. [Are NLP Models really able to Solve Simple Math Word Problems?](https://md.mova.elamur.ai/free/en/docs/papers/patel-2021-are-nlp-models-really-able/) *— our card · 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
23. [Are Rotten Apples Edible? Challenging Commonsense Inference Ability with Exceptions](https://aclanthology.org/2021.findings-acl.181/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
24. [Assessing the Generalization Capacity of Pre-trained Language Models through Japanese Adversarial Natural Language Inference](https://aclanthology.org/2021.blackboxnlp-1.26/) *— 2021 · Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP*
25. [Avoiding Inference Heuristics in Few-shot Prompt-based Finetuning](https://aclanthology.org/2021.emnlp-main.713/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
26. [Back to Square One: Artifact Detection, Training and Commonsense Disentanglement in the Winograd Schema](https://aclanthology.org/2021.emnlp-main.819/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
27. [Benchmarking Machine Reading Comprehension: A Psychological Perspective](https://aclanthology.org/2021.eacl-main.137/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
28. [COM2SENSE: A Commonsense Reasoning Benchmark with Complementary Sentences](https://aclanthology.org/2021.findings-acl.78/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
29. [Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond](https://arxiv.org/abs/2109.00725) *— 2021 · arXiv:2109.00725*
30. [Changing the World by Changing the Data](https://aclanthology.org/2021.acl-long.170/) *— 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)*
31. [Context-aware Adversarial Training for Name Regularity Bias in Named Entity Recognition](https://aclanthology.org/2021.tacl-1.36/) *— 2021 · Transactions of the Association for Computational Linguistics*
32. [Contrastive Explanations for Model Interpretability](https://aclanthology.org/2021.emnlp-main.120/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
33. [Counterfactual Interventions Reveal the Causal Effect of Relative Clause Representations on Agreement Prediction](https://aclanthology.org/2021.conll-1.15/) *— 2021 · Proceedings of the 25th Conference on Computational Natural Language Learning*
34. [Debiasing Methods in Natural Language Understanding Make Bias More Accessible](https://aclanthology.org/2021.emnlp-main.116/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
35. [Diagnosing the First-Order Logical Reasoning Ability Through LogicNLI](https://aclanthology.org/2021.emnlp-main.303/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
36. [Does Putting a Linguist in the Loop Improve NLU Data Collection?](https://aclanthology.org/2021.findings-emnlp.421/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
37. [Doing Good or Doing Right? Exploring the Weakness of Commonsense Causal Reasoning Models](https://aclanthology.org/2021.acl-short.20/) *— 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)*
38. [Don''t Discard All the Biased Instances: Investigating a Core Assumption in Dataset Bias Mitigation Techniques](https://aclanthology.org/2021.findings-emnlp.405/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
39. [Dynabench: Rethinking Benchmarking in NLP](https://aclanthology.org/2021.naacl-main.324/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
40. [ESC: Redesigning WSD with Extractive Sense Comprehension](https://aclanthology.org/2021.naacl-main.371/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
41. [Effective Batching for Recurrent Neural Network Grammars](https://aclanthology.org/2021.findings-acl.380/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
42. [End-to-End Self-Debiasing Framework for Robust NLU Training](https://aclanthology.org/2021.findings-acl.168/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
43. [Evaluating Document Coherence Modeling](https://aclanthology.org/2021.tacl-1.38/) *— 2021 · Transactions of the Association for Computational Linguistics*
44. [Evaluating Entity Disambiguation and the Role of Popularity in Retrieval-Based NLP](https://aclanthology.org/2021.acl-long.345/) *— 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)*
45. [Evaluation Examples are not Equally Informative: How should that change NLP Leaderboards?](https://aclanthology.org/2021.acl-long.346/) *— 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)*
46. [ExCAR: Event Graph Knowledge Enhanced Explainable Causal Reasoning](https://aclanthology.org/2021.acl-long.183/) *— 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. [ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoning](https://aclanthology.org/2021.emnlp-main.609/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
48. [Explaining Contextualization in Language Models using Visual Analytics](https://aclanthology.org/2021.acl-long.39/) *— 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)*
49. [Explanation-Based Human Debugging of NLP Models: A Survey](https://aclanthology.org/2021.tacl-1.90/) *— 2021 · Transactions of the Association for Computational Linguistics*
50. [Exploring Lexical Irregularities in Hypothesis-Only Models of Natural Language Inference](https://arxiv.org/abs/2101.07397) *— 2021 · arXiv:2101.07397*
51. [Exploring Transitivity in Neural NLI Models through Veridicality](https://aclanthology.org/2021.eacl-main.78/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
52. [Factual Probing Is MASK: Learning vs. Learning to Recall](https://aclanthology.org/2021.naacl-main.398/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
53. [FastIF: Scalable Influence Functions for Efficient Model Interpretation and Debugging](https://aclanthology.org/2021.emnlp-main.808/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
54. [Gender Bias in Machine Translation](https://aclanthology.org/2021.tacl-1.51/) *— 2021 · Transactions of the Association for Computational Linguistics*
55. [Generalising to German Plural Noun Classes, from the Perspective of a Recurrent Neural Network](https://aclanthology.org/2021.conll-1.8/) *— 2021 · Proceedings of the 25th Conference on Computational Natural Language Learning*
56. [Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics](https://aclanthology.org/2021.insights-1.18/) *— 2021 · Proceedings of the Second Workshop on Insights from Negative Results in NLP*
57. [Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence](https://aclanthology.org/2021.naacl-main.52/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
58. [HILDIF: Interactive Debugging of NLI Models Using Influence Functions](https://aclanthology.org/2021.internlp-1.1/) *— 2021 · Proceedings of the First Workshop on Interactive Learning for Natural Language Processing*
59. [HateCheck: Functional Tests for Hate Speech Detection Models](https://aclanthology.org/2021.acl-long.4/) *— 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)*
60. [HiddenCut: Simple Data Augmentation for Natural Language Understanding with Better Generalizability](https://aclanthology.org/2021.acl-long.338/) *— 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)*
61. [How Fast can BERT Learn Simple Natural Language Inference?](https://aclanthology.org/2021.eacl-main.51/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
62. [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*
63. [Implicitly Abusive Language – What does it actually look like and why are we not getting there?](https://aclanthology.org/2021.naacl-main.48/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
64. [Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation](https://aclanthology.org/2021.emnlp-main.696/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
65. [IndoNLI: A Natural Language Inference Dataset for Indonesian](https://aclanthology.org/2021.emnlp-main.821/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
66. [Influence Tuning: Demoting Spurious Correlations via Instance Attribution and Instance-Driven Updates](https://aclanthology.org/2021.findings-emnlp.374/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
67. [Investigating Transfer Learning in Multilingual Pre-trained Language Models through Chinese Natural Language Inference](https://aclanthology.org/2021.findings-acl.331/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
68. [Investigating the Effect of Natural Language Explanations on Out-of-Distribution Generalization in Few-shot NLI](https://aclanthology.org/2021.insights-1.17/) *— 2021 · Proceedings of the Second Workshop on Insights from Negative Results in NLP*
69. [Is Incoherence Surprising? Targeted Evaluation of Coherence Prediction from Language Models](https://aclanthology.org/2021.naacl-main.328/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
70. [Is Supervised Syntactic Parsing Beneficial for Language Understanding Tasks? An Empirical Investigation](https://aclanthology.org/2021.eacl-main.270/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
71. [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)*
72. [Knowledgeable or Educated Guess? Revisiting Language Models as Knowledge Bases](https://aclanthology.org/2021.acl-long.146/) *— 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)*
73. [Look at that! BERT can be easily distracted from paying attention to morphosyntax](https://aclanthology.org/2021.scil-1.3/) *— 2021 · Proceedings of the Society for Computation in Linguistics 2021*
74. [MATE-KD: Masked Adversarial TExt, a Companion to Knowledge Distillation](https://aclanthology.org/2021.acl-long.86/) *— 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)*
75. [Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little](https://aclanthology.org/2021.emnlp-main.230/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
76. [Measuring Association Between Labels and Free-Text Rationales](https://aclanthology.org/2021.emnlp-main.804/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
77. [Measuring and Improving Consistency in Pretrained Language Models](https://aclanthology.org/2021.tacl-1.60/) *— 2021*
78. [MedNLI Is Not Immune: Natural Language Inference Artifacts in the Clinical Domain](https://aclanthology.org/2021.acl-short.129/) *— 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)*
79. [Modeling Event Plausibility with Consistent Conceptual Abstraction](https://aclanthology.org/2021.naacl-main.138/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
80. [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://arxiv.org/abs/2110.08207) *— 2021 · arXiv:2110.08207*
81. [Muppet: Massive Multi-task Representations with Pre-Finetuning](https://aclanthology.org/2021.emnlp-main.468/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
82. [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*
83. [NeuralLog: Natural Language Inference with Joint Neural and Logical Reasoning](https://aclanthology.org/2021.starsem-1.7/) *— 2021 · Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics*
84. [Numerical reasoning in machine reading comprehension tasks: are we there yet?](https://aclanthology.org/2021.emnlp-main.759/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
85. [On the Interaction of Belief Bias and Explanations](https://aclanthology.org/2021.findings-acl.259/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
86. [On the Interplay Between Fine-tuning and Composition in Transformers](https://aclanthology.org/2021.findings-acl.201/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
87. [Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models](https://aclanthology.org/2021.acl-long.523/) *— 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)*
88. [Posterior Differential Regularization with f-divergence for Improving Model Robustness](https://aclanthology.org/2021.naacl-main.85/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
89. [Pragmatic competence of pre-trained language models through the lens of discourse connectives](https://aclanthology.org/2021.conll-1.29/) *— 2021 · Proceedings of the 25th Conference on Computational Natural Language Learning*
90. [Probing Language Models for Understanding of Temporal Expressions](https://aclanthology.org/2021.blackboxnlp-1.31/) *— 2021 · Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP*
91. [Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance?](https://aclanthology.org/2021.eacl-main.295/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
92. [Regressive Ensemble for Machine Translation Quality Evaluation](https://aclanthology.org/2021.wmt-1.112/) *— 2021 · Proceedings of the Sixth Conference on Machine Translation*
93. [Robustness Gym: Unifying the NLP Evaluation Landscape](https://aclanthology.org/2021.naacl-demos.6/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations*
94. [SELFEXPLAIN: A Self-Explaining Architecture for Neural Text Classifiers](https://aclanthology.org/2021.emnlp-main.64/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
95. [SICK-NL: A Dataset for Dutch Natural Language Inference](https://aclanthology.org/2021.eacl-main.126/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
96. [SICKNL: A Dataset for Dutch Natural Language Inference](https://arxiv.org/abs/2101.05716) *— 2021 · arXiv:2101.05716*
97. [SPECTRA: Sparse Structured Text Rationalization](https://aclanthology.org/2021.emnlp-main.525/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
98. [Self-Supervised Test-Time Learning for Reading Comprehension](https://aclanthology.org/2021.naacl-main.95/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
99. [Semantic Data Set Construction from Human Clustering and Spatial Arrangement](https://aclanthology.org/2021.cl-1.4/) *— 2021 · Computational Linguistics*
100. [Separating Retention from Extraction in the Evaluation of End-to-end Relation Extraction](https://aclanthology.org/2021.emnlp-main.816/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*

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