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Gender bias in natural language processing (NLP) applications, particularlymachine translation, has been receiving increasing attention. Much of theresearch on this issue has focused on mitigating gender bias in English NLPmodels and systems. Addressing the problem in poorly resourced, and/ormorphologically rich languages has lagged behind, largely due to the lack ofdatasets and resources. In this paper, we introduce a new corpus for genderidentification and rewriting in contexts involving one or two target users (Iand/or You) -- first and second grammatical persons with independentgrammatical gender preferences. We focus on Arabic, a gender-markingmorphologically rich language. The corpus has multiple parallel components:four combinations of 1st and 2nd person in feminine and masculine grammaticalgenders, as well as English, and English to Arabic machine translation output.This corpus expands on Habash et al. (2019)'s Arabic Parallel Gender Corpus(APGC v1.0) by adding second person targets as well as increasing the totalnumber of sentences over 6.5 times, reaching over 590K words. Our new datasetwill aid the research and development of gender identification, controlled textgeneration, and post-editing rewrite systems that could be used to personalizeNLP applications and provide users with the correct outputs based on theirgrammatical gender preferences. We make the Arabic Parallel Gender Corpus (APGCv2.0) publicly available.

The Arabic Parallel Gender Corpus 2.0: Extensions and Analyses