IITP at MEDIQA 2019: Systems Report for Natural Language Inference, Question Entailment and Question Answering
This work addresses medical NLP tasks for healthcare applications, but it is incremental as it applies existing methods to a new dataset without major innovations.
The paper tackled three medical NLP tasks (Natural Language Inference, Recognizing Question Entailment, and Question Answering) in the MEDIQA challenge, achieving highest performances of 81.8%, 53.2%, and 71.7%, respectively, using multiple deep learning systems.
This paper presents the experiments accomplished as a part of our participation in the MEDIQA challenge, an (Abacha et al., 2019) shared task. We participated in all the three tasks defined in this particular shared task. The tasks are viz. i. Natural Language Inference (NLI) ii. Recognizing Question Entailment(RQE) and their application in medical Question Answering (QA). We submitted runs using multiple deep learning based systems (runs) for each of these three tasks. We submitted five system results in each of the NLI and RQE tasks, and four system results for the QA task. The systems yield encouraging results in all three tasks. The highest performance obtained in NLI, RQE and QA tasks are 81.8%, 53.2%, and 71.7%, respectively.