DiaLex: A Benchmark for Evaluating Multidialectal Arabic Word Embeddings
This benchmark addresses the problem of thoroughly evaluating multidialectal Arabic word embeddings, which is crucial for researchers and developers working with Arabic NLP.
This paper introduces DiaLex, a new benchmark for intrinsically evaluating dialectal Arabic word embeddings across five dialects (Algerian, Egyptian, Lebanese, Syrian, Tunisian) and six syntactic/semantic relations. The authors used DiaLex to evaluate existing and newly developed Arabic word embeddings, demonstrating its utility.
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.