IITP at MEDIQA 2019: Systems Report for Natural Language Inference, Question Entailment and Question Answering
June 14, 2019 ยท Declared Dead ยท ๐ BioNLP@ACL
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Authors
Dibyanayan Bandyopadhyay, Baban Gain, Tanik Saikh, Asif Ekbal
arXiv ID
1906.06332
Category
cs.CL: Computation & Language
Cross-listed
cs.AI
Citations
4
Venue
BioNLP@ACL
Last Checked
5 months ago
Abstract
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.
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