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SemEval-2020 Task 4: Commonsense Validation and Explanation

arXiv 2007.00236 ↗First posted 1 Jul 2020cs.CL cs.AIcited by 42

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Abstract (arXiv)

In this paper, we present SemEval-2020 Task 4, Commonsense Validation and Explanation (ComVE), which includes three subtasks, aiming to evaluate whether a system can distinguish a natural language statement that makes sense to humans from one that does not, and provide the reasons. Specifically, in our first subtask, the participating systems are required to choose from two natural language statements of similar wording the one that makes sense and the one does not. The second subtask additionally asks a system to select the key reason from three options why a given statement does not make sense. In the third subtask, a participating system needs to generate the reason. We finally attracted 39 teams participating at least one of the three subtasks. For Subtask A and Subtask B, the performances of top-ranked systems are close to that of humans. However, for Subtask C, there is still a relatively large gap between systems and human performance. The dataset used in our task can be found at https://github.com/wangcunxiang/SemEval2020- Task4-Commonsense-Validation-and-Explanation; The leaderboard can be found at https://competitions.codalab.org/competitions/21080#results.

Key facts. Cited by 42 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Jul 2020.

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Citing papers per year: 2021: 9; 2022: 7; 2023: 9; 2024: 10; 2025: 4; 2026: 3.

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  1. 2023Faithfulness Tests for Natural Language Explanationscited by 107
  2. 2024MEDEC: A Benchmark for Medical Error Detection and Correction in Clinical Notescited by 77
  3. 2021Can Generative Pre-trained Language Models Serve as Knowledge Bases for Closed-book QA?cited by 62
  4. 2023Vera: A General-Purpose Plausibility Estimation Model for Commonsense Statementscited by 44
  5. 2021COM2SENSE: A Commonsense Reasoning Benchmark with Complementary Sentencescited by 40
  6. 2024Aioli: A Unified Optimization Framework for Language Model Data Mixingcited by 33
  7. 2024VidEgoThink: Assessing Egocentric Video Understanding Capabilities for Embodied AIcited by 30
  8. 2024The Probabilities Also Matter: A More Faithful Metric for Faithfulness of Free-Text Explanations in Large Language Modelscited by 29
  9. 2021Knowledge-Grounded Self-Rationalization via Extractive and Natural Language Explanationscited by 24
  10. 2022Diversifying Content Generation for Commonsense Reasoning with Mixture of Knowledge Graph Expertscited by 19
  11. 2024Improving Diversity of Commonsense Generation by Large Language Models via In-Context Learningcited by 19
  12. 2021Exploring Generalization Ability of Pretrained Language Models on Arithmetic and Logical Reasoningcited by 17
30 more
  1. 2021Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations in a Label-Abundant Setupcited by 16
  2. 2023Crystal: Introspective Reasoners Reinforced with Self-Feedbackcited by 15
  3. 2022Retrieval Augmentation for Commonsense Reasoning: A Unified Approachcited by 14
  4. 2024Towards Faithful Natural Language Explanations: A Study Using Activation Patching in Large Language Modelscited by 14
  5. 2023Are Human Explanations Always Helpful? Towards Objective Evaluation of Human Natural Language Explanationscited by 13
  6. 2021Do Natural Language Explanations Represent Valid Logical Arguments? Verifying Entailment in Explainable NLI Gold Standardscited by 12
  7. 2023Abductive Commonsense Reasoning Exploiting Mutually Exclusive Explanationscited by 11
  8. 2024CIF-Bench: A Chinese Instruction-Following Benchmark for Evaluating the Generalizability of Large Language Modelscited by 11
  9. 2021Constrained Text Generation with Global Guidance -- Case Study on CommonGencited by 10
  10. 2025Self-Critique and Refinement for Faithful Natural Language Explanationscited by 9
  11. 2024PromptMind Team at MEDIQA-CORR 2024: Improving Clinical Text Correction with Error Categorization and LLM Ensemblescited by 6
  12. 2022Unsupervised Explanation Generation via Correct Instantiationscited by 5
  13. 2023Few Shot Rationale Generation using Self-Training with Dual Teacherscited by 3
  14. 2024Graph-Guided Textual Explanation Generation Frameworkcited by 3
  15. 2025Towards Transparent AI: A Survey on Explainable Language Modelscited by 3
  16. 2021You Can Do Better! If You Elaborate the Reason When Making Predictioncited by 2
  17. 2021Materialized Knowledge Bases from Commonsense Transformerscited by 2
  18. 2022Autoencoding Language Model Based Ensemble Learning for Commonsense Validation and Explanationcited by 2
  19. 2022Evaluate Confidence Instead of Perplexity for Zero-shot Commonsense Reasoningcited by 1
  20. 2022UU-Tax at SemEval-2022 Task 3: Improving the generalizability of language models for taxonomy classification through data augmentationcited by 1
  21. 2023Explaining Interactions Between Text Spanscited by 1
  22. 2023Formal Proofs as Structured Explanations: Proposing Several Tasks on Explainable Natural Language Inferencecited by 1
  23. 2022Semantic Novelty Detection and Characterization in Factual Text Involving Named Entitiescited by 0
  24. 2023Advancing Transformer's Capabilities in Commonsense Reasoningcited by 0
  25. 2024A Hypothesis-Driven Framework for the Analysis of Self-Rationalising Modelscited by 0
  26. 2025Zero-Shot Commonsense Validation and Reasoning with Large Language Models: An Evaluation on SemEval-2020 Task 4 Datasetcited by 0
  27. 2025Modelling Adjectival Modification Effects on Semantic Plausibilitycited by 0
  28. 2026WeNLEX: Weakly Supervised Natural Language Explanations for Multilabel Chest X-ray Classificationcited by 0
  29. 2026Training Large Language Models for Self-Explanation Faithfulnesscited by 0
  30. 2026Attribute-Based Activation Steering of LLMs for Group-Specific Explanation Generationcited by 0