A Prompt Response to the Demand for Automatic Gender-Neutral Translation
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911773842472960 |
|---|---|
| author | Savoldi, Beatrice Piergentili, Andrea Fucci, Dennis Negri, Matteo Bentivogli, Luisa |
| author_facet | Savoldi, Beatrice Piergentili, Andrea Fucci, Dennis Negri, Matteo Bentivogli, Luisa |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_06041 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Prompt Response to the Demand for Automatic Gender-Neutral Translation Savoldi, Beatrice Piergentili, Andrea Fucci, Dennis Negri, Matteo Bentivogli, Luisa Computation and Language 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. |
| title | A Prompt Response to the Demand for Automatic Gender-Neutral Translation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2402.06041 |