This item is non-discoverable
Paraphrasing Arabiuc Metaphor with Neural Machine Translation
Date
2018-11-17
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
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
ntal results across datasets confirm that neural paraphrases significantly outperform those obtained with
Description
Keywords
Neural Machine Translation; Paraphrasing; Metaphor; Arabic language
Citation
Alkhatib, M. and Shaalan, K. (2018) “Paraphrasing Arabic Metaphor with Neural Machine Translation,” Procedia Computer Science, 142, pp. 308–314.