2021 IEEE International Conference on Big Data (Big Data) 2021
DOI: 10.1109/bigdata52589.2021.9671868
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E-MIMIC: Empowering Multilingual Inclusive Communication

Abstract: Preserving diversity and inclusion is becoming a compelling need in both industry and academia. The ability to use appropriate forms of writing, speaking, and gestures is not widespread even in formal communications such as public calls, public announcements, official reports, and legal documents. The improper use of linguistic expressions can foment unacceptable forms of exclusion, stereotypes as well as forms of verbal violence against minorities, including women. Furthermore, existing machine translation to… Show more

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Cited by 3 publications
(3 citation statements)
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“…Brandl, Cui and Søgaard (2022) focused on neopronouns, showing that language models have difficulties in processing them in Swedish (hen), Danish (de/høn) and English (they/xe). Others focused on standard neutral solutions, for both text classification (Attanasio et al, 2021) and natural language generation tasks, such as genderneutral rewriting (Sun et al, 2021;Vanmassenhove et al, 2021;Attanasio et al, 2021), which consists in converting gendered forms into their gender-neutral counterparts (e.g., En. he/she into singular they, chairman into chairperson).…”
Section: Gender (Bias) and Machine Translationmentioning
confidence: 99%
“…Brandl, Cui and Søgaard (2022) focused on neopronouns, showing that language models have difficulties in processing them in Swedish (hen), Danish (de/høn) and English (they/xe). Others focused on standard neutral solutions, for both text classification (Attanasio et al, 2021) and natural language generation tasks, such as genderneutral rewriting (Sun et al, 2021;Vanmassenhove et al, 2021;Attanasio et al, 2021), which consists in converting gendered forms into their gender-neutral counterparts (e.g., En. he/she into singular they, chairman into chairperson).…”
Section: Gender (Bias) and Machine Translationmentioning
confidence: 99%
“…To do so, we devised a three-step approach that allowed for a more controlled generation procedure with reduced risk of noise (for full details, see Appendix C.1). First, similarly to Attanasio et al (2021), we manually created 800 triplets of neutral, masculine, and feminine referents (e.g. the neighbours: il vicinato -i vicinile vicine).…”
Section: Settingmentioning
confidence: 99%
“…Emerging research has highlighted the importance of reshaping gender in NLP technologies in a more inclusive manner (Dev et al, 2021), also through the representation of non-binary identities and language (Wagner and Zarrieß, 2022;Lauscher et al, 2022;Ovalle et al, 2023). Foundational works in this area have included several applications, such as coreference resolution systems (Cao and Brandl et al, 2022), intra-lingual fair rewriters (Amrhein et al, 2023), and automatic classification of gender-neutral text (Attanasio et al, 2021).…”
Section: Introductionmentioning
confidence: 99%