Buscar

Stress Test Evaluation of Biomedical Word Embeddings

RL1, Publisher: arXiv, Link>


AUTHORS

Vladimir Araujo, Andrés Carvallo, Carlos Aspillaga, Camilo Thorne, Denis Parra


ABSTRACT

The success of pretrained word embeddings has motivated their use in the biomedical domain, with contextualized embeddings yielding remarkable results in several biomedical NLP tasks. However, there is a lack of research on quantifying their behavior under severe "stress" scenarios. In this work, we systematically evaluate three language models with adversarial examples -- automatically constructed tests that allow us to examine how robust the models are. We propose two types of stress scenarios focused on the biomedical named entity recognition (NER) task, one inspired by spelling errors and another based on the use of synonyms for medical terms. Our experiments with three benchmarks show that the performance of the original models decreases considerably, in addition to revealing their weaknesses and strengths. Finally, we show that adversarial training causes the models to improve their robustness and even to exceed the original performance in some cases.

0 visualizaciones

Entradas Recientes

Ver todo

RL2, Publisher: Journal of Machine Learning Research, Link> AUTHORS Jorge Pérez, Pablo Barceló, Javier Marinkovic ABSTRACT Alternatives to recurrent neural networks, in particular, architectures bas

RL2, Publisher: https://github.com/pdm-book/community Link> AUTHORS Marcelo Arenas, Pablo Barceló, Leonid Libkin, Wim Martens, Andreas Pieris ABSTRACT This is a release of parts 1, 2, and 4 of the