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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