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Vicomtech at eHealth-KD Challenge 2020: Deep End-to-End Model for Entity and Relation Extraction in Medical Text

This paper describes the participation of the Vicomtech NLP team in the eHealth-KD 2020 shared task about detecting and classifying entities and relations in health-related texts written in Spanish. The proposed system consists of a single end-to-end deep neural network with pre-trained BERT models as the core for the semantic representation of the input texts. We have experimented with two models: BERT-Base Multilingual Cased and BETO, a BERT model pre-trained on Spanish text. Our system models all the output variables—entities and relations—at the same time, modelling the whole problem jointly. Some of the outputs are fed back to latter layers of the model, connecting the outcomes of the different subtasks in a pipeline fashion. Our system shows robust results in all the scenarios of the task. It has achieved the first position in the main scenario of the competition and top-3 in the rest of the scenarios.

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