UU_TAILS at MEDIQA 2019: Learning Textual Entailment in the Medical Domain
Noha S. Tawfik, Marco René Spruit · 2019
This article describes the participation of the UU TAILS team in the 2019 MEDIQA challenge intended to improve domain-specific models in medical and clinical NLP.The challenge consists of 3 tasks: medical language inference (NLI), recognizing textual entailment (RQE) and question answering (QA).Our team participated in tasks 1 and 2 and our best runs achieved a performance accuracy of 0.852 and 0.584 respectively for the test sets.The models proposed for task 1 relied on BERT embeddings and different ensemble techniques.For the RQE task, we trained a traditional multilayer perceptron network based on embeddings generated by the universal sentence encoder.