SemEval-2020 Task 5: Counterfactual Recognition
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Abstract (arXiv)
We present a counterfactual recognition (CR) task, the shared Task 5 of SemEval-2020. Counterfactuals describe potential outcomes (consequents) produced by actions or circumstances that did not happen or cannot happen and are counter to the facts (antecedent). Counterfactual thinking is an important characteristic of the human cognitive system; it connects antecedents and consequents with causal relations. Our task provides a benchmark for counterfactual recognition in natural language with two subtasks. Subtask-1 aims to determine whether a given sentence is a counterfactual statement or not. Subtask-2 requires the participating systems to extract the antecedent and consequent in a given counterfactual statement. During the SemEval-2020 official evaluation period, we received 27 submissions to Subtask-1 and 11 to Subtask-2. The data, baseline code, and leaderboard can be found at https://competitions.codalab.org/competitions/21691. The data and baseline code are also available at https://zenodo.org/record/3932442.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Aug 2020.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 4; 2024: 3; 2025: 3.
12 papers.
- 2023Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Taskscited by 243
- 2023RECALL: A Benchmark for LLMs Robustness against External Counterfactual Knowledgecited by 51
- 2021I Wish I Would Have Loved This One, But I Didn't -- A Multilingual Dataset for Counterfactual Detection in Product Reviewscited by 44
- 2021Exploring the Efficacy of Automatically Generated Counterfactuals for Sentiment Analysiscited by 30
- 2024The Odyssey of Commonsense Causality: From Foundational Benchmarks to Cutting-Edge Reasoningcited by 5
- 2025Exploring the Hidden Reasoning Process of Large Language Models by Misleading Themcited by 3
- 2023Enhancing Language Representation with Constructional Information for Natural Language Understandingcited by 2
- 2023Assessing Logical Reasoning Capabilities of Encoder-Only Transformer Modelscited by 2
- 2024POLygraph: Polish Fake News Datasetcited by 1
- 2024Topic-aware Causal Intervention for Counterfactual Detectioncited by 1
- 2025Investigating Counterclaims in Causality Extraction from Textcited by 1
- 2025How Well Do LLMs Understand Drug Mechanisms? A Knowledge + Reasoning Evaluation Datasetcited by 1
- 2025How Well Do LLMs Understand Drug Mechanisms? A Knowledge + Reasoning Evaluation Dataset9 Nov 2025
- 2025Investigating Counterclaims in Causality Extraction from Text9 Oct 2025
- 2025Exploring the Hidden Reasoning Process of Large Language Models by Misleading Them20 Mar 2025
- 2024Topic-aware Causal Intervention for Counterfactual Detection25 Sep 2024
- 2024POLygraph: Polish Fake News Dataset1 Jul 2024
- 2024The Odyssey of Commonsense Causality: From Foundational Benchmarks to Cutting-Edge Reasoning27 Jun 2024
- 2023Assessing Logical Reasoning Capabilities of Encoder-Only Transformer Models18 Dec 2023
- 2023RECALL: A Benchmark for LLMs Robustness against External Counterfactual Knowledge14 Nov 2023
- 2023Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks5 Jul 2023
- 2023Enhancing Language Representation with Constructional Information for Natural Language Understanding5 Jun 2023
- 2021Exploring the Efficacy of Automatically Generated Counterfactuals for Sentiment Analysis29 Jun 2021
- 2021I Wish I Would Have Loved This One, But I Didn't -- A Multilingual Dataset for Counterfactual Detection in Product Reviews14 Apr 2021