A Prompt Response to the Demand for Automatic Gender-Neutral Translation

Autor:	Savoldi, Beatrice, Piergentili, Andrea, Fucci, Dennis, Negri, Matteo, Bentivogli, Luisa
Rok vydání:	2024
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	Gender-neutral translation (GNT) that avoids biased and undue binary assumptions is a pivotal challenge for the creation of more inclusive translation technologies. Advancements for this task in Machine Translation (MT), however, are hindered by the lack of dedicated parallel data, which are necessary to adapt MT systems to satisfy neutral constraints. For such a scenario, large language models offer hitherto unforeseen possibilities, as they come with the distinct advantage of being versatile in various (sub)tasks when provided with explicit instructions. In this paper, we explore this potential to automate GNT by comparing MT with the popular GPT-4 model. Through extensive manual analyses, our study empirically reveals the inherent limitations of current MT systems in generating GNTs and provides valuable insights into the potential and challenges associated with prompting for neutrality. Comment: Accepted at EACL 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2402.06041 Zobrazit plný text záznamu View this record from Arxiv