2 papers
cs.CL2024
Prompting Large Language Models with Human Error Markings for Self-Correcting Machine Translation
Nathaniel Berger, Stefan Riezler, Miriam Exel +1
While large language models (LLMs) pre-trained on massive amounts of unpaired language data have reached the state-of-the-art in machine translation (MT) of general domain texts, p…
cs.CL2023
Enhancing Supervised Learning with Contrastive Markings in Neural Machine Translation Training
Nathaniel Berger, Miriam Exel, Matthias Huck +1
Supervised learning in Neural Machine Translation (NMT) typically follows a teacher forcing paradigm where reference tokens constitute the conditioning context in the model's predi…