Abstract
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.- Anthology ID:
- W19-5053
- Volume:
- Proceedings of the 18th BioNLP Workshop and Shared Task
- Month:
- August
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Dina Demner-Fushman, Kevin Bretonnel Cohen, Sophia Ananiadou, Junichi Tsujii
- Venue:
- BioNLP
- SIG:
- SIGBIOMED
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 493–499
- Language:
- URL:
- https://aclanthology.org/W19-5053
- DOI:
- 10.18653/v1/W19-5053
- Cite (ACL):
- Noha Tawfik and Marco Spruit. 2019. UU_TAILS at MEDIQA 2019: Learning Textual Entailment in the Medical Domain. In Proceedings of the 18th BioNLP Workshop and Shared Task, pages 493–499, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- UU_TAILS at MEDIQA 2019: Learning Textual Entailment in the Medical Domain (Tawfik & Spruit, BioNLP 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/W19-5053.pdf