SemEval-2016 Task 3: Community Question Answering
This work addresses the problem of evaluating and advancing community question answering systems for researchers and practitioners, but it is incremental as it builds on prior tasks and benchmarks.
The paper describes the SemEval-2016 Task 3 on Community Question Answering, which included subtasks in English and Arabic for similarity and reranking, with 18 teams participating and achieving MAP scores up to 79.19, significantly outperforming baselines and improving over the 2015 winner by 3 points in accuracy for one subtask.
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.