Gender bias in natural language processing (NLP) applications, particularly machine translation, has been receiving increasing
attention. Much of the research on this issue has focused on mitigating gender bias in English NLP models and systems.
Addressing the problem in poorly resourced, and/or morphologically rich languages has lagged behind, largely due to the
lack of datasets and resources. In this paper, we introduce a new corpus for gender identification and rewriting in contexts
involving one or two target users (I and/or You) – first and second grammatical persons with independent grammatical
gender preferences. We focus on Arabic, a gender-marking morphologically rich language. The corpus has multiple parallel
components: four combinations of 1st and 2nd person in feminine and masculine grammatical genders, 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 total number of sentences over 6.5 times, reaching over
590K words. Our new dataset will aid the research and development of gender identification, controlled text generation, and
post-editing rewrite systems that could be used to personalize NLP applications and provide users with the correct outputs
based on their grammatical gender preferences. We make the Arabic Parallel Gender Corpus (APGC v2.0) publicly available.