2019 ACL ACL 2019

Dr.Quad at MEDIQA 2019: Towards Textual Inference and Question Entailment using contextualized representations

Abstract

AbstractThis paper presents the submissions by TeamDr.Quad to the ACL-BioNLP 2019 shared task on Textual Inference and Question Entailment in the Medical Domain. Our system is based on the prior work Liu et al. (2019) which uses a multi-task objective function for textual entailment. In this work, we explore different strategies for generalizing state-of-the-art language understanding models to the specialized medical domain. Our results on the shared task demonstrate that incorporating domain knowledge through data augmentation is a powerful strategy for addressing challenges posed specialized domains such as medicine.

🌉 Interdisciplinary Bridge — Machine Learning and Natural Language Processing
📈 Trend Setter — Natural Language Inference
🐝 Cross-Pollinator — Artificial Intelligence, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio
🐣 Hot Topic Early Bird — domain knowledge