Community Question Answering forums, such as Quora and Stackoverflow contain millions of questions and answers. Automatically finding the relevant questions from the existing questions and finding the relevant answers to a new question are Natural Language Processing tasks. In this paper, we aim to address these tasks, which we refer to as similar-Question Retrieval and Answer Selection. We present a neural-based model with stacked bidirectional LSTMs and MLP to address these tasks. The model generates the vector representations of the question-question or question-answer pairs and computes their semantic similarity scores, which are then employed to rank and predict relevancies. Extensive experiments demonstrate our results outperform the baselines.

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