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Classifying German Animal Experiment Summaries with Multi-lingual BERT at CLEF eHealth 2019 Task 1

In this paper we present our contribution to the CLEF eHealth challenge 2019, Task 1. The task involves the automatic annotation of German non-technical summaries of animal experiments with ICD-10 codes. We approach the task as multi-label classification problem and leverage the multi-lingual version of the BERT text encoding model [6] to represent the summaries. The model is extended by a single output layer to produce probabilities for individual ICD-10 codes. In addition, we make use of extra training data from the German Clinical Trials Register and ensemble several model instances to improve the overall performance of our approach. We compare our model with five baseline systems including a dictionary matching approach and single-label SVM and BERT classification models. Experiments on the development set highlight the advantage of our approach compared to the baselines with an improvement of 3.6%. Our model achieves the overall best performance in the challenge reaching an F1 score of 0.80 in the final evaluation.