Two questions asking the same thing could be too different in terms of vocabulary and syntactic structure, which makes identifying their semantic equivalence challenging. This study aims to detect semantically equivalent questions in online user forums. We perform an extensive number of experiments using data from two different Stack Exchange forums. We compare standard machine learning methods such as Support Vector Machines (SVM) with a convolutional neural network (CNN). The proposed CNN generates distributed vector representations for pairs of questions and scores them using a similarity metric. We evaluate in-domain word embeddings versus the ones trained with Wikipedia, estimate the impact of the training set size, and evaluate some aspects of domain adaptation. Our experimental results show that the convolutional neural network with in-domain word embeddings achieves high performance even with limited training data.