SemEval-2017 Task 3: Community Question Answering
SemEval-2017 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 Ar...
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| creator | Nakov, Preslav Hoogeveen, Doris Màrquez, Lluís Moschitti, Alessandro Mubarak, Hamdy Baldwin, Timothy Verspoor, Karin |
| description | SemEval-2017 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. |
| doi_str_mv | 10.48550/arxiv.1912.00730 |
| format | Article |
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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.</description><identifier>DOI: 10.48550/arxiv.1912.00730</identifier><language>eng</language><subject>Computer Science - Artificial Intelligence ; Computer Science - Computation and Language ; Computer Science - Information Retrieval ; Computer Science - Learning</subject><creationdate>2019-12</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/1912.00730$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.1912.00730$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Nakov, Preslav</creatorcontrib><creatorcontrib>Hoogeveen, Doris</creatorcontrib><creatorcontrib>Màrquez, Lluís</creatorcontrib><creatorcontrib>Moschitti, Alessandro</creatorcontrib><creatorcontrib>Mubarak, Hamdy</creatorcontrib><creatorcontrib>Baldwin, Timothy</creatorcontrib><creatorcontrib>Verspoor, Karin</creatorcontrib><title>SemEval-2017 Task 3: Community Question Answering</title><description>SemEval-2017 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
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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.</abstract><doi>10.48550/arxiv.1912.00730</doi><oa>free_for_read</oa></addata></record> |
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| identifier | DOI: 10.48550/arxiv.1912.00730 |
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| subjects | Computer Science - Artificial Intelligence Computer Science - Computation and Language Computer Science - Information Retrieval Computer Science - Learning |
| title | SemEval-2017 Task 3: Community Question Answering |
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