arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1906.06332cs.CLcs.AI
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IITP at MEDIQA 2019: Systems Report for Natural Language Inference, Question Entailment and Question Answering

Government College Of Engineering And Textile Technology Berhampore(贝兰普尔政府工程与纺织技术学院);Indian Institute of Technology Patna(印度理工学院巴特那分校)

Dibyanayan Bandyopadhyay, Baban Gain, Tanik Saikh, Asif Ekbal

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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