68 citations · 70 across the 2 of their papers we have counts for
3 papers · 1 filter
How Much Data is Enough Data? Fine-Tuning Large Language Models for In-House Translation: Performance Evaluation Across Multiple Dataset Sizes
Inacio Vieira, Will Allred, Séamus Lankford +2
Decoder-only LLMs have shown impressive performance in MT due to their ability to learn from extensive datasets and generate high-quality translations. However, LLMs often struggle…
A Set of Recommendations for Assessing Human-Machine Parity in Language Translation
Samuel Läubli, Sheila Castilho, Graham Neubig +3
The quality of machine translation has increased remarkably over the past years, to the degree that it was found to be indistinguishable from professional human translation in a nu…
Attaining the Unattainable? Reassessing Claims of Human Parity in Neural Machine Translation
Antonio Toral, Sheila Castilho, Ke Hu +1
We reassess a recent study (Hassan et al., 2018) that claimed that machine translation (MT) has reached human parity for the translation of news from Chinese into English, using pa…