Community Question Answering (CQA) services have evolved into a popular way of online information seeking, where users can interact and exchange knowledge in the form of questions and answers. In this paper, we study the problem of finding historical questions that are semantically equivalent to the queried ones, assuming that the answers to the similar questions should also answer the new ones. The major challenge of question retrieval is the word mismatch problem between questions, as users can formulate the same question using different wording. Most existing methods measure the similarity between questions based on the bag-of-words (BOWs) representation capturing no semantics between words. Therefore, this study proposes to use word embeddings, which can capture semantic and syntactic information from contexts, to vectorize the questions. The questions are clustered using Kmeans to speed up the search and ranking tasks. The similarity between the questions is measured using cosine similarity based on their weighted continuous valued vectors. We run our experiments on real world data set from Yahoo! Answers in English and Arabic to show the efficiency and generality of our proposed method.