From Binary Defaults to Contextual Bias: Translating Queer Morphology with NMT and LLMs

Abstract

This paper evaluates how Neural Machine Translation (NMT) and Large Language Models (LLMs) process non-binary morphology when translating German literary fiction into Italian. We apply an inductive, mixed-methods framework to analyze 12 NMT and 15 LLM translations from a human-in-the-loop experiment. Results reveal a fundamental divergence. NMT defaults to standard binary grammar but applies it inconsistently, often flipping between masculine and feminine forms for the same subject across different sentences, which effectively erases queer visibility. Conversely, LLMs actively attempt gender-fair language via neutralization and neomorphemes (e.g., the schwa). However, LLMs introduce new systematic errors: driven by semantic cues, they exhibit a contextual bias that, in the present study, frequently led to over-feminization, and their attempts to create inclusive word endings result in structurally invalid words. Ultimately, these findings expose current limitations and provide preliminary empirical guidance to assist post-editors in navigating the complex challenges of gender-fair translation.

- Anthology ID:
- 2026.gitt-1.4
- Volume:
- [Proceedings of the 4th Workshop on Gender-Inclusive Translation Technologies (GITT 2026)](https://aclanthology.org/volumes/2026.gitt-1/)
- Month:

• June
• Year:
• 2026
• Address:
• Tilburg, the Netherlands
• Editors:
• Manuel Lardelli ,Beatrice Savoldi ,Janiça Hackenbuchner ,Luisa Bentivogli ,Eleni Gkovedarou ,Joke Daems

- Venues:
- [GITT](https://aclanthology.org/venues/gitt/) |[WS](https://aclanthology.org/venues/ws/)
- SIG:
- Publisher:

• European Association for Machine Translation

- Note:
- Pages:

• 31–48

- Language:
- URL:
- [https://aclanthology.org/2026.gitt-1.4/](https://aclanthology.org/2026.gitt-1.4/)
- DOI:
- Cite (ACL):

• Manuel Lardelli. 2026. From Binary Defaults to Contextual Bias: Translating Queer Morphology with NMT and LLMs . InProceedings of the 4th Workshop on Gender-Inclusive Translation Technologies (GITT 2026) , pages 31–48, Tilburg, the Netherlands. European Association for Machine Translation.

- Cite (Informal):
- [From Binary Defaults to Contextual Bias: Translating Queer Morphology with NMT and LLMs](https://aclanthology.org/2026.gitt-1.4/) (Lardelli, GITT 2026)
- PDF:
- [https://aclanthology.org/2026.gitt-1.4.pdf](https://aclanthology.org/2026.gitt-1.4.pdf)