Add to ORKGVocabulary transfer is a transfer learning subtask in which language models fine-tune with the corpus-specific tokenization instead of the default one, which is being used during pretraining. This usually improves the resulting performance of the model, and in the paper, we demonstrate that vocabulary transfer is especially beneficial for medical text processing. Using three different medical natural language processing datasets, we show vocabulary transfer to provide up to ten extra percentage points for the downstream classifier accuracy
This paper describes the ADAPT Centre’s submissions to the WMT20 Biomedical Translation Shared Task ...
BACKGROUND: Bilingual lexicon induction (BLI) is an important task in the biomedical domain as trans...
As a task requiring strong professional experience as supports, predictive biomedical intelligence c...
Transformers are responsible for the vast majority of recent advances in natural language processing...
Natural Language Processing (NLP) has seen tremendous improvements over the last few years. Transfor...
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on...
© Springer Nature Switzerland AG 2020. Although deep neural networks yield state-of-the-art performa...
The current generation of neural network-based natural language processing models excels at learning...
This article has been published in a revised form in Natural Language Engineering https://doi.org/10...
Health literacy, i.e. the ability to read and understand medical text, is a relevant component of pu...
Expert-layman text style transfer technologies have the potential to improve communication between m...
BACKGROUND: Bilingual lexicon induction (BLI) is an important task in the biomedical domain as trans...
While deep learning techniques have shown promising results in many natural language processing (NLP...
The emergence of deep learning algorithms in natural language processing has boosted the development...
Langnickel L, Fluck J. We are not ready yet: limitations of transfer learning for Disease Named Enti...
This paper describes the ADAPT Centre’s submissions to the WMT20 Biomedical Translation Shared Task ...
BACKGROUND: Bilingual lexicon induction (BLI) is an important task in the biomedical domain as trans...
As a task requiring strong professional experience as supports, predictive biomedical intelligence c...
Transformers are responsible for the vast majority of recent advances in natural language processing...
Natural Language Processing (NLP) has seen tremendous improvements over the last few years. Transfor...
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on...
© Springer Nature Switzerland AG 2020. Although deep neural networks yield state-of-the-art performa...
The current generation of neural network-based natural language processing models excels at learning...
This article has been published in a revised form in Natural Language Engineering https://doi.org/10...
Health literacy, i.e. the ability to read and understand medical text, is a relevant component of pu...
Expert-layman text style transfer technologies have the potential to improve communication between m...
BACKGROUND: Bilingual lexicon induction (BLI) is an important task in the biomedical domain as trans...
While deep learning techniques have shown promising results in many natural language processing (NLP...
The emergence of deep learning algorithms in natural language processing has boosted the development...
Langnickel L, Fluck J. We are not ready yet: limitations of transfer learning for Disease Named Enti...
This paper describes the ADAPT Centre’s submissions to the WMT20 Biomedical Translation Shared Task ...
BACKGROUND: Bilingual lexicon induction (BLI) is an important task in the biomedical domain as trans...
As a task requiring strong professional experience as supports, predictive biomedical intelligence c...