SemEval-2016 Task 3: Community Question Answering
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Abstract (arXiv)
This paper describes the SemEval--2016 Task 3 on Community Question Answering, which we offered in English and Arabic. For English, we had three subtasks: Question--Comment Similarity (subtask A), Question--Question Similarity (B), and Question--External Comment Similarity (C). For Arabic, we had another subtask: Rerank the correct answers for a new question (D). Eighteen teams participated in the task, submitting a total of 95 runs (38 primary and 57 contrastive) for the four subtasks. A variety of approaches and features were used by the participating systems to address the different subtasks, which are summarized in this paper. The best systems achieved an official score (MAP) of 79.19, 76.70, 55.41, and 45.83 in subtasks A, B, C, and D, respectively. These scores are significantly better than those for the baselines that we provided. For subtask A, the best system improved over the 2015 winner by 3 points absolute in terms of Accuracy.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Dec 2019.
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Citing papers per year: 2021: 6; 2022: 2; 2023: 3; 2024: 0; 2025: 1.
12 papers.
- 2021QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehensioncited by 91
- 2021Biomedical Question Answering: A Survey of Approaches and Challengescited by 46
- 2021Overview of the CLEF--2021 CheckThat! Lab on Detecting Check-Worthy Claims, Previously Fact-Checked Claims, and Fake Newscited by 23
- 2023ReadMe++: Benchmarking Multilingual Language Models for Multi-Domain Readability Assessmentcited by 19
- 2025MedArabiQ: Benchmarking Large Language Models on Arabic Medical Taskscited by 15
- 2021PerCQA: Persian Community Question Answering Datasetcited by 5
- 2023QUADRo: Dataset and Models for QUestion-Answer Database Retrievalcited by 5
- 2023Bridging Continuous and Discrete Spaces: Interpretable Sentence Representation Learning via Compositional Operationscited by 5
- 2021HeteroQA: Learning towards Question-and-Answering through Multiple Information Sources via Heterogeneous Graph Modelingcited by 4
- 2021Can Taxonomy Help? Improving Semantic Question Matching using Question Taxonomycited by 1
- 2022Learning Semantic Textual Similarity via Topic-informed Discrete Latent Variablescited by 0
- 2022Unsupervised Question Duplicate and Related Questions Detection in e-learning platformscited by 0
- 2025MedArabiQ: Benchmarking Large Language Models on Arabic Medical Tasks6 May 2025
- 2023Bridging Continuous and Discrete Spaces: Interpretable Sentence Representation Learning via Compositional Operations24 May 2023
- 2023ReadMe++: Benchmarking Multilingual Language Models for Multi-Domain Readability Assessment23 May 2023
- 2023QUADRo: Dataset and Models for QUestion-Answer Database Retrieval30 Mar 2023
- 2022Unsupervised Question Duplicate and Related Questions Detection in e-learning platforms20 Dec 2022
- 2022Learning Semantic Textual Similarity via Topic-informed Discrete Latent Variables7 Nov 2022
- 2021HeteroQA: Learning towards Question-and-Answering through Multiple Information Sources via Heterogeneous Graph Modeling27 Dec 2021
- 2021PerCQA: Persian Community Question Answering Dataset25 Dec 2021
- 2021Overview of the CLEF--2021 CheckThat! Lab on Detecting Check-Worthy Claims, Previously Fact-Checked Claims, and Fake News23 Sep 2021
- 2021QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension27 Jul 2021
- 2021Biomedical Question Answering: A Survey of Approaches and Challenges10 Feb 2021
- 2021Can Taxonomy Help? Improving Semantic Question Matching using Question Taxonomy20 Jan 2021