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home / docs / papers / cited-by / Who cites «Breaking NLI Systems with Sentences that Require Simple Lexical Inferences» · page 2

Who cites «Breaking NLI Systems with Sentences that Require Simple Lexical Inferences» · page 2

202 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. ← the card

Pages: 1 · 2 · 3

  1. Decomposing and Comparing Meaning Relations: Paraphrasing, Textual Entailment, Contradiction, and Specificity — 2020 · Proceedings of the Twelfth Language Resources and Evaluation Conference
  2. Detecting Word Sense Disambiguation Biases in Machine Translation for Model-Agnostic Adversarial Attacks — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
  3. Discriminatively-Tuned Generative Classifiers for Robust Natural Language Inference — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
  4. Do Neural Models Learn Systematicity of Monotonicity Inference in Natural Language? — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
  5. Evaluating Models' Local Decision Boundaries via Contrast Sets — our card · 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020
  6. Evaluation of Coreference Resolution Systems Under Adversarial Attacks — 2020 · Proceedings of the First Workshop on Computational Approaches to Discourse
  7. Experience Grounds Language — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
  8. GenAug: Data Augmentation for Finetuning Text Generators — 2020 · Proceedings of Deep Learning Inside Out (DeeLIO): The First Workshop on Knowledge Extraction and Integration for Deep Learning Architectures
  9. How does Punctuation Affect Neural Models in Natural Language Inference — 2020 · Proceedings of the Probability and Meaning Conference (PaM 2020)
  10. Hy-NLI: a Hybrid system for Natural Language Inference — 2020 · Proceedings of the 28th International Conference on Computational Linguistics
  11. HypoNLI: Exploring the Artificial Patterns of Hypothesis-only Bias in Natural Language Inference — 2020 · Proceedings of the Twelfth Language Resources and Evaluation Conference
  12. INFOTABS: Inference on Tables as Semi-structured Data — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
  13. Iterative Feature Mining for Constraint-Based Data Collection to Increase Data Diversity and Model Robustness — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
  14. Knowledge-Enhanced Natural Language Inference Based on Knowledge Graphs — 2020 · Proceedings of the 28th International Conference on Computational Linguistics
  15. Language (Re)modelling: Towards Embodied Language Understanding — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
  16. Learning as Abduction: Trainable Natural Logic Theorem Prover for Natural Language Inference — 2020 · Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics
  17. Mining Knowledge for Natural Language Inference from Wikipedia Categories — 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020
  18. More Bang for Your Buck: Natural Perturbation for Robust Question Answering — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
  19. Neural Natural Language Inference Models Partially Embed Theories of Lexical Entailment and Negation — our card · 2020 · Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP
  20. On the Importance of Adaptive Data Collection for Extremely Imbalanced Pairwise Tasks — 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020
  21. On the Systematicity of Probing Contextualized Word Representations: The Case of Hypernymy in BERT — 2020 · Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics
  22. Robustness to Modification with Shared Words in Paraphrase Identification — 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020
  23. Shortcut Learning in Deep Neural Networks — 2020 · arXiv:2004.07780
  24. Stance Detection Benchmark: How Robust Is Your Stance Detection? — 2020 · arXiv:2001.01565
  25. Tasty Burgers, Soggy Fries: Probing Aspect Robustness in Aspect-Based Sentiment Analysis — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
  26. The Curse of Performance Instability in Analysis Datasets: Consequences, Source, and Suggestions — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
  27. Towards Debiasing NLU Models from Unknown Biases — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
  28. Towards Robustifying NLI Models Against Lexical Dataset Biases — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
  29. Translation Artifacts in Cross-lingual Transfer Learning — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
  30. What Does My QA Model Know? Devising Controlled Probes Using Expert Knowledge — 2020 · Transactions of the Association for Computational Linguistics
  31. Word-level Textual Adversarial Attacking as Combinatorial Optimization — 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
  32. A Logic-Driven Framework for Consistency of Neural Models — 2019 · arXiv:1909.00126
  33. A logical-based corpus for cross-lingual evaluation — 2019 · Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)
  34. Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation — 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)
  35. Adversarial Analysis of Natural Language Inference Systems — 2019 · arXiv:1912.03441
  36. Analysis Methods in Neural Language Processing: A Survey — 2019 · Transactions of the Association for Computational Linguistics
  37. Are We Modeling the Task or the Annotator? An Investigation of Annotator Bias in Natural Language Understanding Datasets — 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)
  38. BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions — 2019 · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
  39. Can Neural Networks Understand Monotonicity Reasoning? — 2019 · Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP
  40. Combating Adversarial Misspellings with Robust Word Recognition — 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
  41. Composition of Sentence Embeddings: Lessons from Statistical Relational Learning — 2019 · Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)
  42. Deep Learning for Natural Language Inference — 2019 · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorials
  43. Diversify Your Datasets: Analyzing Generalization via Controlled Variance in Adversarial Datasets — 2019 · Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
  44. Do Nuclear Submarines Have Nuclear Captains? A Challenge Dataset for Commonsense Reasoning over Adjectives and Objects — 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)
  45. Don''t Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biases — 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)
  46. Don''t Take the Premise for Granted: Mitigating Artifacts in Natural Language Inference — 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
  47. EQUATE: A Benchmark Evaluation Framework for Quantitative Reasoning in Natural Language Inference — our card · 2019 · Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
  48. Explaining Simple Natural Language Inference — 2019 · Proceedings of the 13th Linguistic Annotation Workshop
  49. HELP: A Dataset for Identifying Shortcomings of Neural Models in Monotonicity Reasoning — 2019 · Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)
  50. HellaSwag: Can a Machine Really Finish Your Sentence? — 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
  51. How Reasonable are Common-Sense Reasoning Tasks: A Case-Study on the Winograd Schema Challenge and SWAG — 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)
  52. Inoculation by Fine-Tuning: A Method for Analyzing Challenge Datasets — 2019 · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
  53. Learning the Difference that Makes a Difference with Counterfactually-Augmented Data — 2019 · arXiv:1909.12434
  54. Mining Discourse Markers for Unsupervised Sentence Representation Learning — 2019 · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
  55. Misleading Failures of Partial-input Baselines — 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
  56. Multi-Task Deep Neural Networks for Natural Language Understanding — 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
  57. Neural Text Summarization: A Critical Evaluation — 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)
  58. PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification — 2019 · arXiv:1908.11828
  59. PAWS: Paraphrase Adversaries from Word Scrambling — 2019 · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
  60. Posing Fair Generalization Tasks for Natural Language Inference — 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)
  61. Probing Natural Language Inference Models through Semantic Fragments — 2019 · arXiv:1909.07521
  62. Probing Neural Network Comprehension of Natural Language Arguments — 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
  63. Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches — 2019 · arXiv:1904.01172
  64. SherLIiC: A Typed Event-Focused Lexical Inference Benchmark for Evaluating Natural Language Inference — our card · 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
  65. Shifting the Baseline: Single Modality Performance on Visual Navigation & QA — 2019 · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
  66. Understanding Roles and Entities: Datasets and Models for Natural Language Inference — 2019 · arXiv:1904.09720
  67. Unlearn Dataset Bias in Natural Language Inference by Fitting the Residual — 2019 · Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)
  68. pair2vec: Compositional Word-Pair Embeddings for Cross-Sentence Inference — 2019 · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
  69. AdvEntuRe: Adversarial Training for Textual Entailment with Knowledge-Guided Examples — 2018 · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  70. Analyzing Compositionality-Sensitivity of NLI Models — 2018 · arXiv:1811.07033
  71. Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation — 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
  72. Frame- and Entity-Based Knowledge for Common-Sense Argumentative Reasoning — 2018 · Proceedings of the 5th Workshop on Argument Mining
  73. How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks — our card · 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
  74. Improving Large-Scale Fact-Checking using Decomposable Attention Models and Lexical Tagging — 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
  75. SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference — 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
  76. Stress-Testing Neural Models of Natural Language Inference with Multiply-Quantified Sentences — 2018 · arXiv:1810.13033
  77. The Importance of Being Recurrent for Modeling Hierarchical Structure — 2018 · arXiv:1803.03585
  78. e-SNLI: Natural Language Inference with Natural Language Explanations — 2018 · arXiv:1812.01193
  79. abzianidze-2023-formal-proofs-structured-explanations-proposing
  80. abzianidze-2023-spacenli-evaluating-consistency-predicting-inferences
  81. banerjee-2024-inffeed-influence-functions-as-feedback
  82. chung-2025-divlogiceval-framework-benchmarking-logical-reasoning
  83. elazar-2020-extraordinary-failure-complement-coercion-crowdsourcing
  84. fiacco-2019-deep-neural-model-inspection-comparison
  85. gabriel-2022-naturaladversaries-can-naturalistic-adversaries-be
  86. gemechu-2024-external-knowledge-driven-argument-mining
  87. gokhale-2022-textitgeneralized-but-not-robust-comparing
  88. gupta-2024-evaluating-concurrent-robustness-language-models
  89. howard-2022-neurocounterfactuals-beyond-minimal-edit-counterfactuals
  90. k-2022-multilingual-checklist-generation-evaluation
  91. kogkalidis-2024-oyxoy-modern-nlp-test-suite
  92. li-2025-persona-aware-alignment-framework-personalized
  93. li-2025-task-calibration-calibrating-large-language
  94. lin-2024-sentence-space-metrics-ssm-evaluation
  95. madusanka-2025-unravelling-logic-investigating-generalisation-transformers
  96. murugesan-2023-mismatch-fine-grained-evaluation-machine
  97. nguyen-2023-firo-finite-context-indexing-restricted
  98. reich-2022-leveraging-expert-guided-adversarial-augmentation
  99. schmitt-2021-continuous-entailment-patterns-lexical-inference
  100. skandalis-2025-neurosymbolic-ai-natural-language-inference

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