Natural Language Understanding and Generation models suffer from a limited capability of understanding the nuances of inclusive communication as they are trained on massive data, often including significant portions of non-inclusive content. Even when the models are specifically designed to address non-inclusive language detection or reformulation, they disregard, to a large extent, inclusivenessrelated features that are likely correlated with the inclusive language nuances, such as the discourse type, level of inclusiveness, and intended context of use. To assess the importance of additional inclusiveness-related features, we collect a new corpus of Italian administrative documents humanly annotated by linguistic experts. Linguistic experts not only highlight non-inclusive text snippets and propose possible reformulations, but also annotate multi-aspect labels related to different inclusive language nuances. We empirically show that a multi-task learning approach that leverages the multi-aspect annotations can improve the non-inclusive text reformulation performance, thereby confirming the potential of expert-annotated data in inclusive language processing.

Moreno La Quatra, S.G. (2024). Building Foundations for Inclusiveness through Expert-Annotated Data. Acquisgrana : CEUR.

Building Foundations for Inclusiveness through Expert-Annotated Data

Michela Tonti;Rachele Raus;
2024

Abstract

Natural Language Understanding and Generation models suffer from a limited capability of understanding the nuances of inclusive communication as they are trained on massive data, often including significant portions of non-inclusive content. Even when the models are specifically designed to address non-inclusive language detection or reformulation, they disregard, to a large extent, inclusivenessrelated features that are likely correlated with the inclusive language nuances, such as the discourse type, level of inclusiveness, and intended context of use. To assess the importance of additional inclusiveness-related features, we collect a new corpus of Italian administrative documents humanly annotated by linguistic experts. Linguistic experts not only highlight non-inclusive text snippets and propose possible reformulations, but also annotate multi-aspect labels related to different inclusive language nuances. We empirically show that a multi-task learning approach that leverages the multi-aspect annotations can improve the non-inclusive text reformulation performance, thereby confirming the potential of expert-annotated data in inclusive language processing.
2024
Proceedings of the Workshops of the EDBT/ICDT 2024 Joint Conference co-located with the EDBT/ICDT 2024 Joint Conference
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Moreno La Quatra, S.G. (2024). Building Foundations for Inclusiveness through Expert-Annotated Data. Acquisgrana : CEUR.
Moreno La Quatra, Salvatore Greco, Luca Cagliero, Michela Tonti, Francesca Dragotto, Rachele Raus, Stefania Cavagnoli, Tania Cerquitelli
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/967347
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