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Found 14 papers, 3 papers with code
Improving Machine Translation of Rare and Unseen Word Senses
The performance of NMT systems has improved drastically in the past few years but the translation of multi-sense words still poses a challenge.
Language-Family Adapters for Low-Resource Multilingual Neural Machine Translation
Training a new adapter on each language pair or training a single adapter on all language pairs without updating the pretrained model has been proposed as a parameter-efficient alternative.
Improving the Lexical Ability of Pretrained Language Models for Unsupervised Neural Machine Translation
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 sequence generation task, which requires the model to align the lexical- and high-level representations of the two languages.
ContraCAT: Contrastive Coreference Analytical Templates for Machine Translation
Recent high scores on pronoun translation using context-aware neural machine translation have suggested that current approaches work well.
The LMU Munich System for the WMT 2020 Unsupervised Machine Translation Shared Task
Our core unsupervised neural machine translation (UNMT) system follows the strategy of Chronopoulou et al. (2020), using a monolingual pretrained language generation model (on German) and fine-tuning it on both German and Upper Sorbian, before initializing a UNMT model, which is trained with online backtranslation.
Reusing a Pretrained Language Model on Languages with Limited Corpora for Unsupervised NMT
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-the-art results.
Addressing Zero-Resource Domains Using Document-Level Context in Neural Machine Translation
Achieving satisfying performance in machine translation on domains for which there is no training data is challenging.
The LMU Munich Unsupervised Machine Translation System for WMT19
We describe LMU Munich{'}s machine translation system for German→Czech translation which was used to participate in the WMT19 shared task on unsupervised news translation.
Combining Local and Document-Level Context: The LMU Munich Neural Machine Translation System at WMT19
We describe LMU Munich{'}s machine translation system for English→German translation which was used to participate in the WMT19 shared task on supervised news translation.
Coreference and Coherence in Neural Machine Translation: A Study Using Oracle Experiments
We show that NMT models taking advantage of context oracle signals can achieve considerable gains in BLEU, of up to 7. 02 BLEU for coreference and 1. 89 BLEU for coherence on subtitles translation.
LMU Munich's Neural Machine Translation Systems at WMT 2018
The systems were used for our participation in the WMT18 biomedical translation task and in the shared task on machine translation of news.
The LMU Munich Unsupervised Machine Translation Systems
We describe LMU Munich{'}s unsupervised machine translation systems for English↔German translation.
