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 vio- lence against minorities, including women. Furthermore, existing machine translation tools are not designed to generate inclusive content. The present paper investigates a joint effort of the research communities of linguistics and Deep Learning Natural Lan- guage Understanding in fighting against non-inclusive, prejudiced language forms. It presents a methodology aimed at tackling the improper use of language in formal communication, with a particular attention paid to Romanic languages (Italian, in particular). State-of-the-art Deep Language Modeling architec- tures are exploited to automatically identify non-inclusive text snippets, suggest alternative forms, and produce inclusive text rephrasing. A preliminary evaluation conducted on a benchmark dataset shows promising results, i.e., 85% accuracy in predicting inclusive/non-inclusive communications. Index Terms—Inclusive Language, Gender Equality, Natural Language Processing, Deep Learning.

E-MIMIC: Empowering Multilingual Inclusive Communication

Rachele Raus;Michela Tonti;
2021

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 vio- lence against minorities, including women. Furthermore, existing machine translation tools are not designed to generate inclusive content. The present paper investigates a joint effort of the research communities of linguistics and Deep Learning Natural Lan- guage Understanding in fighting against non-inclusive, prejudiced language forms. It presents a methodology aimed at tackling the improper use of language in formal communication, with a particular attention paid to Romanic languages (Italian, in particular). State-of-the-art Deep Language Modeling architec- tures are exploited to automatically identify non-inclusive text snippets, suggest alternative forms, and produce inclusive text rephrasing. A preliminary evaluation conducted on a benchmark dataset shows promising results, i.e., 85% accuracy in predicting inclusive/non-inclusive communications. Index Terms—Inclusive Language, Gender Equality, Natural Language Processing, Deep Learning.
2021 IEEE International Conference on Big Data (Big Data)
4227
4234
Rachele Raus; Michela Tonti; Tania Cerquitelli; Salvatore Greco; Moreno La Quatra; Giuseppe Attanasio; Luca Cagliero
File in questo prodotto:
Eventuali allegati, non sono esposti

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/853726
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact