text: 426,162,1450376093425360899,1063986211881009153,Bashar Alhafni,"🚨 New Dataset Alert 🚨 We are releasing the Arabic Parallel Gender Corpus v2.0 (APGC v2.0) for gender identification and rewriting in contexts involving one or two target users. Joint work with @nyhabash and @hbouamor at @CamelNlp  Paper: <LINK> (1/n) This corpus expands on its previous version (APGC v1.0) which was introduced by @nyhabash et al. in: <LINK> by adding second person targets as well as increasing the total number of sentences over 6.5 times (~80K sentences), reaching over 590K words (2/n) We annotated ~63K Arabic sentences from the English-Arabic OpenSubtitles 2018 based on the genders of their first and second person references. In case a gendered reference exists, we introduce all the possible opposite gender forms (3/n) <LINK> We also provide word-level gender annotations for all the Arabic sentences in the corpus (4/n) <LINK> The corpus has multiple parallel components: four combinations of 1st and 2nd person in feminine and masculine grammatical genders, as well as English (as we got it from OpenSubtitles 2018) (5/n) <LINK> We show that our corpus can also be used to detect and quantify bias in gender-unaware machine translation systems targeting Arabic. We machine translated the English sentences into Arabic and evaluated them based on different gender specificity factors in Arabic and English(6/n) <LINK> So if you're working on gender bias, post-editing MT output, or personalization in general, you should check our corpus out! (n/n)",https://arxiv.org/abs/2110.09216,"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. ",The Arabic Parallel Gender Corpus 2.0: Extensions and Analyses,7,"['🚨 New Dataset Alert 🚨\n\nWe are releasing the Arabic Parallel Gender Corpus v2.0 (APGC v2.0) for gender identification and rewriting in contexts involving one or two target users. Joint work with @nyhabash and @hbouamor at @CamelNlp \n\nPaper: <LINK> (1/n)', 'This corpus expands on its previous version (APGC v1.0) which was introduced by @nyhabash et al. in: https://t.co/FTvNPDEFhQ by adding second person targets as well as increasing the total number of sentences over 6.5 times (~80K sentences), reaching over 590K words (2/n)', 'We annotated ~63K Arabic sentences from the English-Arabic OpenSubtitles 2018 based on the genders of their first and second person references. In case a gendered reference exists, we introduce all the possible opposite gender forms (3/n) https://t.co/sp4nK1H7Pq', 'We also provide word-level gender annotations for all the Arabic sentences in the corpus (4/n) https://t.co/1dYkIxTeb7', 'The corpus has multiple parallel components: four combinations of 1st and 2nd person in feminine and masculine grammatical genders, as well as English (as we got it from OpenSubtitles 2018) (5/n) https://t.co/hQ6LhyACbY', 'We show that our corpus can also be used to detect and quantify bias in gender-unaware machine translation systems targeting Arabic. We machine translated the English sentences into Arabic and evaluated them based on different gender specificity factors in Arabic and English(6/n) https://t.co/ioZVWwQggJ', ""So if you're working on gender bias, post-editing MT output, or personalization in general, you should check our corpus out! (n/n)""]",21,10,1476