  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.
