---
title: "SemEval-2017 Task 3: Community Question Answering (2017)"
description: "This paper reports on the third edition of the SemEval shared task on Community Question… 89 references, cited by 74 papers here."
authors: ["Preslav Nakov", "Doris Hoogeveen", "Lluís Màrquez", "Alessandro Moschitti", "Hamdy Mubarak", "Timothy Baldwin", "Karin Verspoor"]
year: 2017
venue: "Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)"
source: "https://aclanthology.org/S17-2003/"
doi: "10.18653/v1/s17-2003"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/nakov-2017-semeval-2017-task-3-community/"
---
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# SemEval-2017 Task 3: Community Question Answering

**Authors:** [Preslav Nakov](https://md.research.elamur.ai/free/en/authors/preslav-nakov/), Doris Hoogeveen, [Lluís Màrquez](https://md.research.elamur.ai/free/en/authors/lluis-marquez/), [Alessandro Moschitti](https://md.research.elamur.ai/free/en/authors/alessandro-moschitti/), [Hamdy Mubarak](https://md.research.elamur.ai/free/en/authors/hamdy-mubarak/), [Timothy Baldwin](https://md.research.elamur.ai/free/en/authors/timothy-baldwin/) and [Karin Verspoor](https://md.research.elamur.ai/free/en/authors/karin-verspoor/) · **Year:** 2017 · **Venue:** [Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)](https://md.research.elamur.ai/free/en/venues/semeval/) ([SemEval 2017](https://md.research.elamur.ai/free/en/venues/semeval/2017/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/S17-2003/) · [PDF](https://aclanthology.org/S17-2003.pdf) · [DOI 10.18653/v1/s17-2003](https://doi.org/10.18653/v1/s17-2003) · [OpenAlex](https://openalex.org/W2915240437)

**Citations:** 188 citations (OpenAlex, 2026-10-14) · [74](https://md.research.elamur.ai/free/en/papers/nakov-2017-semeval-2017-task-3-community/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
This paper reports on the third edition of the SemEval shared task on Community Question Answering (CQA). The task targets web forums such as Qatar Living, where open posting produces long, noisy threads that are hard for users to read. The organizers reran the four 2016 subtasks: (A) ranking comments in a thread by relevance to its question, (B) reranking retrieved questions by similarity to a new question, (C) ranking comments from other threads as answers to a new question, and (D) an Arabic answer-reranking subtask built on medical forums. Training data came from the 2015 and 2016 editions, and new test sets were created. A new subtask E added large-scale, multi-domain duplicate question detection on StackExchange data (CQADupStack). Twenty-three teams took part. The top-performing approaches relied either on neural models built on distributed representations or on support vector machines using syntactic kernels, and they beat the baselines clearly on A–C. No team entered subtask E, which the authors attribute to its data volume. The released annotated datasets are a lasting resource for CQA research in English and Arabic.

## Key points
- 23 teams submitted 85 runs (36 primary, 49 contrastive) for subtasks A–D, compared with 18 teams and 95 runs in 2016.
- The best official MAP scores were 88.43 (A), 47.22 (B), 15.46 (C) and 61.16 (D).
- Subtask E asked systems to rerank 50 candidate questions per query using CQADupStack (StackExchange subforums), but it received no submissions.
- Data covers English (Qatar Living) and Arabic (three medical forums), labeled through crowdsourcing. The test set for D was prepared with a slightly changed procedure.
- Notable systems:
  - GW QA, best on D, used WTMF latent models trained partly on Arabic Gigaword, with MADAMIRA preprocessing.
  - bunji, second on C, combined decomposable-attention features in a CRF.
  - Talla did well on B with a random-forest pairwise-preference model of 2,000 estimators.

## Interesting ideas
- **Similarity triangle decomposition.** The main problem (new question → external comment) is split into two easier pairwise problems: question–question and question–comment within a thread. Each can be modeled and evaluated separately and then combined, which suits any retrieval-then-answer pipeline.
- **Modeling missing words for short texts.** WTMF uses words absent from a short text as a signal of what the text is not about. This reduces sparsity and could help with short Ukrainian forum posts or product titles.
- **"Plausibility" features.** These check whether a comment actually tries to answer, rather than making a side remark or asking for clarification. Such cheap, task-specific cues are useful for filtering noisy user-generated content.
- **Bilingual domain lexicon plus machine translation.** UPC-USMBA translated Arabic data into English and used a lexicon of body parts, drugs and diseases in both languages. This lets a lower-resource language reuse English tools, a pattern that transfers to Ukrainian.
- **Rerunning a shared task.** Repeating a newly designed task with fresh test data while keeping old data for training gives participants a second, better-informed attempt and produces comparable results across years.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [The University of Melbourne](https://md.research.elamur.ai/free/en/universities/the-university-of-melbourne/) — University, Melbourne, Australia · 60 works · 21,199 citations
- [Hamad bin Khalifa University](https://md.research.elamur.ai/free/en/universities/hamad-bin-khalifa-university/) — University, Doha, Qatar · 71 works · 12,942 citations

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## Cite

Cite as: Preslav Nakov, Doris Hoogeveen, Lluís Màrquez, Alessandro Moschitti, Hamdy Mubarak, Timothy Baldwin and Karin Verspoor. 2017. SemEval-2017 Task 3: Community Question Answering. Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{nakov2017semeval,
  title = {{SemEval-2017 Task 3: Community Question Answering}},
  author = {Preslav Nakov, Doris Hoogeveen, Lluís Màrquez, Alessandro Moschitti, Hamdy Mubarak, Timothy Baldwin and Karin Verspoor},
  booktitle = {Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)},
  year = {2017},
  doi = {10.18653/v1/s17-2003},
  url = {https://doi.org/10.18653/v1/s17-2003}
}
```

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70. Kateryna Tymoshenko, Daniele Bonadiman, and Alessandro Moschitti. 2016. Learning to rank non-factoid answers: Comment selection in web forums. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. Indianapolis, Indiana, USA, CIKM ’16, pages 2049–2052. [find](https://scholar.google.com/scholar?q=Kateryna+Tymoshenko%2C+Daniele+Bonadiman%2C+and+Alessandro+Moschitti%2E+2016%2E+Learning+to+rank+non%2Dfactoid+answers%3A+Comment+selection+in+web+forums%2E+In+Proceedings+of+the+25th+ACM+International+on+Conferenc)
71. Yufei Xie, Maoquan Wang, Jing Ma, Jian Jiang, and Zhao Lu. 2017. EICA team at SemEval-2017 task 3: Semantic and metadata-based features for Community Question Answering. In Proceedings of the International Workshop on Semantic Evaluation. Vancouver, Canada, SemEval ’17, pages 293–299. [find](https://scholar.google.com/scholar?q=Yufei+Xie%2C+Maoquan+Wang%2C+Jing+Ma%2C+Jian+Jiang%2C+and+Zhao+Lu%2E+2017%2E+EICA+team+at+SemEval%2D2017+task+3%3A+Semantic+and+metadata%2Dbased+features+for+Community+Question+Answering%2E+In+Proceedings+of+the+Internat)
72. Filip S̆aina, Toni Kukurin, Lukrecija Puljić, Mladen Karan, and Jan S̆najder. 2017. TakeLab-QA at SemEval-2017 task 3: Classification experiments for answer retrieval in community QA. In Proceedings of the Workshop on Semantic Evaluation. Vancouver, Canada, SemEval ’17, pages 339–343. [find](https://scholar.google.com/scholar?q=Filip+S%CC%86aina%2C+Toni+Kukurin%2C+Lukrecija+Puljic%CC%81%2C+Mladen+Karan%2C+and+Jan+S%CC%86najder%2E+2017%2E+TakeLab%2DQA+at+SemEval%2D2017+task+3%3A+Classification+experiments+for+answer+retrieval+in+community+QA%2E+In+Proceedings+)
73. Xuchen Yao, Benjamin Van Durme, Chris Callison-Burch, and Peter Clark. 2013. Answer extraction as sequence tagging with tree edit distance. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics. NAACL-HLT ’13, pages 858–867. [find](https://scholar.google.com/scholar?q=Xuchen+Yao%2C+Benjamin+Van+Durme%2C+Chris+Callison%2DBurch%2C+and+Peter+Clark%2E+2013%2E+Answer+extraction+as+sequence+tagging+with+tree+edit+distance%2E+In+Proceedings+of+the+Conference+of+the+North+American+Chapt)
74. Di Wang and Eric Nyberg. 2015. A long short-term memory model for answer sentence selection in question answering. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing. Beijing, China, ACL… [find](https://scholar.google.com/scholar?q=Di+Wang+and+Eric+Nyberg%2E+2015%2E+A+long+short%2Dterm+memory+model+for+answer+sentence+selection+in+question+answering%2E+In+Proceedings+of+the+53rd+Annual+Meeting+of+the+Association+for+Computational+Lingui)
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77. Kai Wang, Zhaoyan Ming, and Tat-Seng Chua. 2009. A syntactic tree matching approach to finding similar questions in community-based QA services. In Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval. Boston, Massachusetts, USA, SI-GIR ’09,… [find](https://scholar.google.com/scholar?q=Kai+Wang%2C+Zhaoyan+Ming%2C+and+Tat%2DSeng+Chua%2E+2009%2E+A+syntactic+tree+matching+approach+to+finding+similar+questions+in+community%2Dbased+QA+services%2E+In+Proceedings+of+the+32nd+International+ACM+SIGIR+Conf)
78. Guangyou Zhou, Li Cai, Jun Zhao, and Kang Liu. 2011. Phrase-based translation model for question retrieval in community question answer archives. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics. Portland, Oregon, USA, ACL ’11, pages 653–662. [find](https://scholar.google.com/scholar?q=Guangyou+Zhou%2C+Li+Cai%2C+Jun+Zhao%2C+and+Kang+Liu%2E+2011%2E+Phrase%2Dbased+translation+model+for+question+retrieval+in+community+question+answer+archives%2E+In+Proceedings+of+the+49th+Annual+Meeting+of+the+Assoc)
79. Mengqiu Wang and Christopher D. Manning. 2010. Probabilistic tree-edit models with structured latent variables for textual entailment and question answering. In Proceedings of the 23rd International Conference on Computational Linguistics. Beijing, China, COLING ’10, pages 1164–1172. [find](https://scholar.google.com/scholar?q=Mengqiu+Wang+and+Christopher+D%2E+Manning%2E+2010%2E+Probabilistic+tree%2Dedit+models+with+structured+latent+variables+for+textual+entailment+and+question+answering%2E+In+Proceedings+of+the+23rd+International+C)
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81. Mengqiu Wang, Noah A. Smith, and Teruko Mitamura. 2007. [What is the Jeopardy model? A quasi-synchronous grammar for QA](https://md.research.elamur.ai/free/en/papers/wang-2007-what-is-jeopardy-model-quasi/). In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Prague, Czech Republic, EMNLP-CoNLL ’07…
82. Xiaoqiang Zhou, Baotian Hu, Jiaxin Lin, Yang Xiang, and Xiaolong Wang. 2015b. ICRC-HIT: A deep learning based comment sequence labeling system for answer selection challenge. In Proceedings of the 11th International Workshop on Semantic Evaluation. Denver, Colorado, USA, SemEval ’15, pages 210–214. [find](https://scholar.google.com/scholar?q=Xiaoqiang+Zhou%2C+Baotian+Hu%2C+Jiaxin+Lin%2C+Yang+Xiang%2C+and+Xiaolong+Wang%2E+2015b%2E+ICRC%2DHIT%3A+A+deep+learning+based+comment+sequence+labeling+system+for+answer+selection+challenge%2E+In+Proceedings+of+the+11t)
83. Guoshun Wu, Yixuan Sheng adn Man Lan, and Yuanbin Wu. 2017a. ECNU at SemEval-2017 task 3: Using traditional and deep learning methods to address community question answering task. In Proceedings of the Workshop on Semantic Evaluation. Vancouver, Canada, SemEval ’17, pages 365–369. [find](https://scholar.google.com/scholar?q=Guoshun+Wu%2C+Yixuan+Sheng+adn+Man+Lan%2C+and+Yuanbin+Wu%2E+2017a%2E+ECNU+at+SemEval%2D2017+task+3%3A+Using+traditional+and+deep+learning+methods+to+address+community+question+answering+task%2E+In+Proceedings+of+th)
84. Team ID Team Affiliation Beihang-MSRA Beihang University, Beijing, China; Microsoft Research, Beijing, China (Wu et al., 2017b) bunji Hitachi Ltd., Japan (Koreeda et al., 2017) ECNU East China Normal University, P.R. China; Shanghai Key Laboratory of Multidimensional Information Processing, P.R. [find](https://scholar.google.com/scholar?q=Team+ID+Team+Affiliation+Beihang%2DMSRA+Beihang+University%2C+Beijing%2C+China%3B+Microsoft+Research%2C+Beijing%2C+China+%28Wu+et+al%2E%2C+2017b%29+bunji+Hitachi+Ltd%2E%2C+Japan+%28Koreeda+et+al%2E%2C+2017%29+ECNU+East+China+Normal+)
85. China (Wu et al., 2017a) EICA East China Normal University, Shanghai, P.R.China (Xie et al., 2017) FuRongWang National University of Defense Technology, P.R. [find](https://scholar.google.com/scholar?q=China+%28Wu+et+al%2E%2C+2017a%29+EICA+East+China+Normal+University%2C+Shanghai%2C+P%2ER%2EChina+%28Xie+et+al%2E%2C+2017%29+FuRongWang+National+University+of+Defense+Technology%2C+P%2ER%2E)
86. China (Zhang et al., 2017) FA3L University of Pisa, Italy (Attardi et al., 2017) The George Washington University, D.C. [find](https://scholar.google.com/scholar?q=China+%28Zhang+et+al%2E%2C+2017%29+FA3L+University+of+Pisa%2C+Italy+%28Attardi+et+al%2E%2C+2017%29+The+George+Washington+University%2C+D%2EC%2E)
87. USA GW QA (Almarwani and Diab, 2017) IIT-UHH Indian Institute of Technology Patna, India; University of Hamburg, Germany (Nandi et al., 2017) KeLP University of Roma, Tor Vergata, Italy; Qatar Computing Research Institute, HBKU, Qatar (Filice et al., 2017) MoRS Universidade de Lisboa, Portugal (Rodr… [find](https://scholar.google.com/scholar?q=USA+GW+QA+%28Almarwani+and+Diab%2C+2017%29+IIT%2DUHH+Indian+Institute+of+Technology+Patna%2C+India%3B+University+of+Hamburg%2C+Germany+%28Nandi+et+al%2E%2C+2017%29+KeLP+University+of+Roma%2C+Tor+Vergata%2C+Italy%3B+Qatar+Computi)
88. China (Qi et al., 2017) SimBow Orange Labs, France (Charlet and Damnati, 2017) SnowMan Harbin Institute of Technology, P.R. China [no paper submitted] SwissAlps Zurich University of Applied Sciences, Switzerland (Deriu and Cieliebak, 2017) TakeLab-QA University of Zagreb, Croatia (S̆aina et al., 201… [find](https://scholar.google.com/scholar?q=China+%28Qi+et+al%2E%2C+2017%29+SimBow+Orange+Labs%2C+France+%28Charlet+and+Damnati%2C+2017%29+SnowMan+Harbin+Institute+of+Technology%2C+P%2ER%2E+China+%5Bno+paper+submitted%5D+SwissAlps+Zurich+University+of+Applied+Sciences%2C+)
89. Submission KeLP-primary Beihang-MSRA-primary Beihang-MSRA-contrastive2 Beihang-MSRA-contrastive1 IIT-UHH-primary ECNU-contrastive1 ECNU-primary EICA-contrastive2 bunji-primary EICA-primary EICA-contrastive1 IIT-UHH-contrastive1 SwissAlps-primary SwissAlps-contrastive1 bunji-contrastive1 IIT-UHH-cont… [find](https://scholar.google.com/scholar?q=Submission+KeLP%2Dprimary+Beihang%2DMSRA%2Dprimary+Beihang%2DMSRA%2Dcontrastive2+Beihang%2DMSRA%2Dcontrastive1+IIT%2DUHH%2Dprimary+ECNU%2Dcontrastive1+ECNU%2Dprimary+EICA%2Dcontrastive2+bunji%2Dprimary+EICA%2Dprimary+EICA%2Dcontra)

*Updated 24 October 2026*