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The task of recognizing and generating paraphrases is an essential component in many Arabic natural language processing (NLP) applications. A well-established machine translation approach for automatically extracting paraphrases, leverages bilingual corpora to find the equivalent meaning of phrases in a single language, is performed by "pivoting" over a shared translation in another language. Neural machine translation has recently become a viable alternative approach to the more widely-used statistical machine translation. In this paper, we revisit bilingual pivoting in the context of neural machine translation and present a paraphrasing model based mainly on neural networks. Our model describes paraphrases in a continuous space and generates candidate paraphrases for an Arabic source input. Experimental results across datasets confirm that neural paraphrases significantly outperform those obtained …