AraWEAT: Multidimensional Analysis of Biases in Arabic Word Embeddings

By Anne Lauscher, Rafik Takieddin, Simone Paolo Ponzetto, Goran Glavas. 2020

33 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Natural Language Processing, Safety, Robustness & Interpretability

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Citations in the corpus

Re-contextualizing Fairness in NLP: The Case of India (2022, background): “Recently, fairness research in NLP has also been expanded to non-English languages such as Arabic (Lauscher et al., 2020), Japanese (Takeshita et al.”
RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models (2021, methodology): “…proposed extensions to further embedding models (Liang et al., 2020a,b) and languages (e.g., McCurdy and Serbetci, 2020; Lauscher and Glavaˇs, 2019; Lauscher et al., 2020b; May et al., 2019), analyses of the proposed measures (e.g., Gonen and Goldberg, 2019; Ethayarajh et al., 2019), more…”
Socially Aware Bias Measurements for Hindi Language Representations (2021, unknown): “Some recent work tackle the challenges of societal biases in language representations coming from various cultures and languages such as Arabic, French, Spanish, Chinese, and German (Lauscher et al., 2020; Ch´avez Mulsa and Spanakis, 2020; Kurpicz-Briki, 2020; Zhou et al., 2019; Zhao et al., 2020; L”