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Community Question Answering (CQA) has become a primary means for people toacquire knowledge, where people are free to ask questions or submit answers. Toenhance the efficiency of the service, similar question identification becomesa core task in CQA which aims to find a similar question from the archivedrepository whenever a new question is asked. However, it has long been achallenge to properly measure the similarity between two questions due to theinherent variation of natural language, i.e., there could be different ways toask a same question or different questions sharing similar expressions. Toalleviate this problem, it is natural to involve the existing answers for theenrichment of the archived questions. Traditional methods typically take aone-side usage, which leverages the answer as some expanded representation ofthe corresponding question. Unfortunately, this may introduce unexpected noisesinto the similarity computation since answers are often long and diverse,leading to inferior performance. In this work, we propose a two-side usage,which leverages the answer as a bridge of the two questions. The key idea isbased on our observation that similar questions could be addressed by similarparts of the answer while different questions may not. In other words, we cancompare the matching patterns of the two questions over the same answer tomeasure their similarity. In this way, we propose a novel matching overmatching model, namely Match$^2$, which compares the matching patterns betweentwo question-answer pairs for similar question identification. Empiricalexperiments on two benchmark datasets demonstrate that our model cansignificantly outperform previous state-of-the-art methods on the similarquestion identification task.

Match$^2$: A Matching over Matching Model for Similar Question Identification