4 citations · 9 across the 5 of their papers we have counts for
5 papers
Language-Family Adapters for Low-Resource Multilingual Neural Machine Translation
Alexandra Chronopoulou, Dario Stojanovski, Alexander Fraser
Large multilingual models trained with self-supervision achieve state-of-the-art results in a wide range of natural language processing tasks. Self-supervised pretrained models are…
Improving the Lexical Ability of Pretrained Language Models for Unsupervised Neural Machine Translation
Alexandra Chronopoulou, Dario Stojanovski, Alexander Fraser
Successful methods for unsupervised neural machine translation (UNMT) employ crosslingual pretraining via self-supervision, often in the form of a masked language modeling or a seq…
The LMU Munich System for the WMT 2020 Unsupervised Machine Translation Shared Task
Alexandra Chronopoulou, Dario Stojanovski, Viktor Hangya +1
This paper describes the submission of LMU Munich to the WMT 2020 unsupervised shared task, in two language directions, German<->Upper Sorbian. Our core unsupervised neural machine…
Reusing a Pretrained Language Model on Languages with Limited Corpora for Unsupervised NMT
Alexandra Chronopoulou, Dario Stojanovski, Alexander Fraser
Using a language model (LM) pretrained on two languages with large monolingual data in order to initialize an unsupervised neural machine translation (UNMT) system yields state-of-…
Addressing Zero-Resource Domains Using Document-Level Context in Neural Machine Translation
Dario Stojanovski, Alexander Fraser
Achieving satisfying performance in machine translation on domains for which there is no training data is challenging. Traditional supervised domain adaptation is not suitable for…