76 citations · 148 across the 26 of their papers we have counts for
7 papers · 1 filter
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-…
Anchor-based Bilingual Word Embeddings for Low-Resource Languages
Tobias Eder, Viktor Hangya, Alexander Fraser
Good quality monolingual word embeddings (MWEs) can be built for languages which have large amounts of unlabeled text. MWEs can be aligned to bilingual spaces using only a few thou…
Pragmatic information in translation: a corpus-based study of tense and mood in English and German
Anita Ramm, Ekaterina Lapshinova-Koltunski, Alexander Fraser
Grammatical tense and mood are important linguistic phenomena to consider in natural language processing (NLP) research. We consider the correspondence between English and German t…
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…
Towards Reasonably-Sized Character-Level Transformer NMT by Finetuning Subword Systems
Jindřich Libovický, Alexander Fraser
Applying the Transformer architecture on the character level usually requires very deep architectures that are difficult and slow to train. These problems can be partially overcome…
On the Language Neutrality of Pre-trained Multilingual Representations
Jindřich Libovický, Rudolf Rosa, Alexander Fraser
Multilingual contextual embeddings, such as multilingual BERT and XLM-RoBERTa, have proved useful for many multi-lingual tasks. Previous work probed the cross-linguality of the rep…