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
Henning Sebastian Schäfer, Ahmad Idrissi-Yaghir, Péter Horn, Christoph M. Friedrich · 2022
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 stateof-the-art models in natural language processing (NLP) to a low-resource language clinical corpus.Two directions are evaluated: (a) English models can be applied to translated texts to subsequently transfer the predicted annotations to the source language and (b) existing high-quality annotations can be transferred beyond translation and then used to train NLP models in the target language.Effectiveness and loss of transmission is evaluated using the German Berlin-Tübingen-Oncology Corpus (BRONCO) dataset with transferred external data from NCBI disease, SemEval-2013 drug-drug interaction (DDI) and i2b2/VA 2010 data.The use of English models for translated clinical texts has always involved attempts to take full advantage of the benefits associated with them (large pre-trained biomedical word embeddings).To improve advances in this area, we provide a general-purpose pipeline to transfer any annotated BRAT or CoNLL format to various target languages.For the entity class medication, good results were obtained with 0.806 F 1-score after re-alignment.Limited success occurred in the diagnosis and treatment class with results just below 0.5 F 1-score due to differences in annotation guidelines.