Community Question Answering (CQA) has become a primary means for people to
acquire knowledge, where people are free to ask questions or submit answers. To
enhance the efficiency of the service, similar question identification becomes
a core task in CQA which aims to find a similar question from the archived
repository whenever a new question is asked. However, it has long been a
challenge to properly measure the similarity between two questions due to the
inherent variation of natural language, i.e., there could be different ways to
ask a same question or different questions sharing similar expressions. To
alleviate this problem, it is natural to involve the existing answers for the
enrichment of the archived questions. Traditional methods typically take a
one-side usage, which leverages the answer as some expanded representation of
the corresponding question. Unfortunately, this may introduce unexpected noises
into 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 is
based on our observation that similar questions could be addressed by similar
parts of the answer while different questions may not. In other words, we can
compare the matching patterns of the two questions over the same answer to
measure their similarity. In this way, we propose a novel matching over
matching model, namely Match$^2$, which compares the matching patterns between
two question-answer pairs for similar question identification. Empirical
experiments on two benchmark datasets demonstrate that our model can
significantly outperform previous state-of-the-art methods on the similar
question identification task.

在社区问答中，为了更高效地服务于人们，提出了一种新的匹配模型 Match$^2$，可以通过比较两个问题的匹配模式来度量它们的相似度，该模型在 similar question identification 任务中表现优于以往的方法。