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Community Question Answering (cQA) is one of the popular Natural Language Processing (NLP) problems being targeted by researchers across the globe. Couple of the unanswered questions in the domain of cQA are ‘can we label the questions/answers as factual or not?’ and ‘Is the given answer by the user to a particular factual question is correct and if it is correct, can we measure the correctness and factuality of the given answer?’. We have participated in SemEval-2019 Task 8 which deals with these questions. In this paper, we present the features used, approaches followed for feature engineering, models experimented with and finally the results. Our primary submission with accuracy (official metric for SemEval Task 8) of 0.65 in Subtask B (Answer Classification) and 0.63 in Subtask A (Question Classification) stood at 6 and 16 places respectively.

CodeForTheChange at SemEval-2019 Task 8: Skip-Thoughts for Fact Checking in Community Question Answering