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Paraphrasing Araboic Metaphor with Neural Machine Translation
dc.contributor.author | Alkhatib, Manar | |
dc.contributor.author | Shaalan, Khaled | |
dc.date.accessioned | 2025-05-15T10:14:03Z | |
dc.date.available | 2025-05-15T10:14:03Z | |
dc.date.issued | 2018-11-17 | |
dc.description.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 | |
dc.identifier.issn | 1877-0509 | |
dc.identifier.uri | https://bspace.buid.ac.ae/handle/1234/3053 | |
dc.language.iso | en_US | |
dc.title | Paraphrasing Araboic Metaphor with Neural Machine Translation | |
dc.type | Article |
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