# SemEval-2016 Task 3: Community Question Answering

arXiv: https://arxiv.org/abs/1912.01972 · HTML: https://mlsanity.com/paper/1912.01972/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2019.

## Built on by (most cited first, 12 of 12)

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- [PerCQA: Persian Community Question Answering Dataset](https://mlsanity.com/paper/2112.13238/) (2021)
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- [Bridging Continuous and Discrete Spaces: Interpretable Sentence Representation Learning via Compositional Operations](https://mlsanity.com/paper/2305.14599/) (2023)
- [HeteroQA: Learning towards Question-and-Answering through Multiple Information Sources via Heterogeneous Graph Modeling](https://mlsanity.com/paper/2112.13597/) (2021)
- [Can Taxonomy Help? Improving Semantic Question Matching using Question Taxonomy](https://mlsanity.com/paper/2101.08201/) (2021)
- [Learning Semantic Textual Similarity via Topic-informed Discrete Latent Variables](https://mlsanity.com/paper/2211.03616/) (2022)
- [Unsupervised Question Duplicate and Related Questions Detection in e-learning platforms](https://mlsanity.com/paper/2301.05150/) (2022)

## 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.