DiaLex: A Benchmark for Evaluating Multidialectal Arabic Word Embeddings
November 22, 2020 Β· Declared Dead Β· π Workshop on Arabic Natural Language Processing
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Authors
Muhammad Abdul-Mageed, Shady Elbassuoni, Jad Doughman, AbdelRahim Elmadany, El Moatez Billah Nagoudi, Yorgo Zoughby, Ahmad Shaher, Iskander Gaba, Ahmed Helal, Mohammed El-Razzaz
arXiv ID
2011.10970
Category
cs.AI: Artificial Intelligence
Citations
4
Venue
Workshop on Arabic Natural Language Processing
Last Checked
4 months ago
Abstract
Word embeddings are a core component of modern natural language processing systems, making the ability to thoroughly evaluate them a vital task. We describe DiaLex, a benchmark for intrinsic evaluation of dialectal Arabic word embedding. DiaLex covers five important Arabic dialects: Algerian, Egyptian, Lebanese, Syrian, and Tunisian. Across these dialects, DiaLex provides a testbank for six syntactic and semantic relations, namely male to female, singular to dual, singular to plural, antonym, comparative, and genitive to past tense. DiaLex thus consists of a collection of word pairs representing each of the six relations in each of the five dialects. To demonstrate the utility of DiaLex, we use it to evaluate a set of existing and new Arabic word embeddings that we developed. Our benchmark, evaluation code, and new word embedding models will be publicly available.
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