AraWEAT: Multidimensional Analysis of Biases in Arabic Word Embeddings
This work addresses biases in Arabic word embeddings, which is important for NLP applications in Arabic-speaking communities, but it is incremental as it applies existing bias tests to a new language.
The paper tackled the problem of biases in Arabic word embeddings by conducting a multidimensional analysis across models, text types, dialects, and time, finding that implicit gender bias in embeddings trained on Arabic news corpora increased steadily from 2007 to 2017.
Recent work has shown that distributional word vector spaces often encode human biases like sexism or racism. In this work, we conduct an extensive analysis of biases in Arabic word embeddings by applying a range of recently introduced bias tests on a variety of embedding spaces induced from corpora in Arabic. We measure the presence of biases across several dimensions, namely: embedding models (Skip-Gram, CBOW, and FastText) and vector sizes, types of text (encyclopedic text, and news vs. user-generated content), dialects (Egyptian Arabic vs. Modern Standard Arabic), and time (diachronic analyses over corpora from different time periods). Our analysis yields several interesting findings, e.g., that implicit gender bias in embeddings trained on Arabic news corpora steadily increases over time (between 2007 and 2017). We make the Arabic bias specifications (AraWEAT) publicly available.