# SemEval-2017 Task 3: Community Question Answering

arXiv: https://arxiv.org/abs/1912.00730 · HTML: https://mlsanity.com/paper/1912.00730/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2019.

## Built on by (most cited first, 13 of 13)

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- [ELQA: A Corpus of Metalinguistic Questions and Answers about English](https://mlsanity.com/paper/2205.00395/) (2022)
- [CPopQA: Ranking Cultural Concept Popularity by LLMs](https://mlsanity.com/paper/2311.07897/) (2023)
- ["Mama Always Had a Way of Explaining Things So I Could Understand'': A Dialogue Corpus for Learning to Construct Explanations](https://mlsanity.com/paper/2209.02508/) (2022)
- [PerCQA: Persian Community Question Answering Dataset](https://mlsanity.com/paper/2112.13238/) (2021)
- [Neural Sequence-to-Sequence Modeling with Attention by Leveraging Deep Learning Architectures for Enhanced Contextual Understanding in Abstractive Text Summarization](https://mlsanity.com/paper/2404.08685/) (2024)
- [Batch-Softmax Contrastive Loss for Pairwise Sentence Scoring Tasks](https://mlsanity.com/paper/2110.15725/) (2021)
- [Modeling the Quality of Dialogical Explanations](https://mlsanity.com/paper/2403.00662/) (2024)
- [Learning Semantic Textual Similarity via Topic-informed Discrete Latent Variables](https://mlsanity.com/paper/2211.03616/) (2022)
- [Answer ranking in Community Question Answering: a deep learning approach](https://mlsanity.com/paper/2212.01218/) (2022)

## Abstract (arXiv)

We describe SemEval-2017 Task 3 on Community Question Answering. This year, we reran the four subtasks from SemEval-2016:(A) Question-Comment Similarity,(B) Question-Question Similarity,(C) Question-External Comment Similarity, and (D) Rerank the correct answers for a new question in Arabic, providing all the data from 2015 and 2016 for training, and fresh data for testing. Additionally, we added a new subtask E in order to enable experimentation with Multi-domain Question Duplicate Detection in a larger-scale scenario, using StackExchange subforums. A total of 23 teams participated in the task, and submitted a total of 85 runs (36 primary and 49 contrastive) for subtasks A-D. Unfortunately, no teams participated in subtask E. A variety of approaches and features were used by the participating systems to address the different subtasks. The best systems achieved an official score (MAP) of 88.43, 47.22, 15.46, and 61.16 in subtasks A, B, C, and D, respectively. These scores are better than the baselines, especially for subtasks A-C.