Learning from Many Voices: Literary MT Using Multi-Reference Human and Synthetic Data

The paper explores how to improve literary machine translation by using datasets that contain multiple valid translations of the same source text. It introduces a filtering framework that selects source texts whose references show meaningful variation while staying faithful, based on semantic similarity. Experiments show that fine‑tuning on medium to high similarity data outperforms low similarity data, and that using only this filtered subset can match or exceed performance achieved with the full unfiltered set. Additionally, the study compares synthetic translations generated by large language models with human expert translations, finding that fine‑tuning on human expert data yields better results in both automatic metrics and human evaluations, underscoring the continued importance of expert translations for literary MT.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

• llms
• nlp
• fine-tuning