Learning from Many Voices: Literary MT Using Multi-Reference Human and Synthetic Data
This work finds fine-tuning on human expert translations outperforms fine-tuning on synthetically augmented data in automatic metrics and human evaluations, demonstrating the indispensable value of human expert translations for fine-tuning literary machine translation models.
Sijing Wu, J. Wieting, David A. Smith
· 7 citations