Community Question Answering (cQA) provides new interesting research directions to the traditional Question Answering (QA) field, e.g., the exploitation of the interaction between users and the structure of related posts. In this context, we organized SemEval-2015 Task 3 on Answer Selection in cQA, which included two subtasks: (a) classifying answers as good, bad, or potentially relevant with respect to the question, and (b) answering a YES/NO question with yes, no, or unsure, based on the list of all answers. We set subtask A for Arabic and English on two relatively different cQA domains, i.e., the Qatar Living website for English, and a Quran-related website for Arabic. We used crowdsourcing on Amazon Mechanical Turk to label a large English training dataset, which we released to the research community. Thirteen teams participated in the challenge with a total of 61 submissions: 24 primary and 37 contrastive. The best systems achieved an official score (macro-averaged F 1 ) of 57.19 and 63.7 for the English subtasks A and B, and 78.55 for the Arabic subtask A.
The SemEval-2015 Task 3, Answer Selection in Community Question Answering, targeted to classify comments in a thread as relevant, potentially useful, or bad, concerning the thread question REF .
SemEval-2015 Task 3: Answer Selection in Community Question Answering
SemEval-2015
Computer Science