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Found 4 papers, 1 papers with code
Cross-Language Transfer of High-Quality Annotations: Combining Neural Machine Translation with Cross-Linguistic Span Alignment to Apply NER to Clinical Texts in a Low-Resource Language
In this work, cross-linguistic span prediction based on contextualized word embedding models is used together with neural machine translation (NMT) to transfer and apply the state-of-the-art models in natural language processing (NLP) to a low-resource language clinical corpus.
Comprehensive Study on German Language Models for Clinical and Biomedical Text Understanding
Recent advances in natural language processing (NLP) can be largely attributed to the advent of pre-trained language models such as BERT and RoBERTa.
On the Impact of Cross-Domain Data on German Language Models
Traditionally, large language models have been either trained on general web crawls or domain-specific data.
Domain Adaptation of Transformer-Based Models using Unlabeled Data for Relevance and Polarity Classification of German Customer Feedback
For the subtask Relevance Classification, the best models achieve a micro-averaged $F1$-Score of 96. 1 % on the first test set and 95. 9 % on the second one, and a score of 85. 1 % and 85. 3 % for the subtask Polarity Classification.
