Paraphrasing Araboic Metaphor with Neural Machine Translation

dc.contributor.authorAlkhatib, Manar
dc.contributor.authorShaalan, Khaled
dc.date.accessioned2025-05-15T10:14:03Z
dc.date.available2025-05-15T10:14:03Z
dc.date.issued2018-11-17
dc.description.abstractThe 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
dc.identifier.issn1877-0509
dc.identifier.urihttps://bspace.buid.ac.ae/handle/1234/3053
dc.language.isoen_US
dc.titleParaphrasing Araboic Metaphor with Neural Machine Translation
dc.typeArticle

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