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Paraphrasing Arabic Metaphor with Neural Machine Translation
Date
2018-11-17
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Abstract
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 with
2018 The Authors. Published by Elsevier B.V.