IITP at MEDIQA 2019: Systems Report for Natural Language Inference, Question Entailment and Question Answering

被引:0
作者
Bandyopadhyay, Dibyanayan [1 ]
Gain, Baban [1 ]
Saikh, Tanik [2 ]
Ekbal, Asif [2 ]
机构
[1] Govt Coll Engn & Text Technol, Berhampur, India
[2] Indian Inst Technol Patna, Patna, Bihar, India
来源
SIGBIOMED WORKSHOP ON BIOMEDICAL NATURAL LANGUAGE PROCESSING (BIONLP 2019) | 2019年
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
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 the three tasks. The highest performance obtained in NLI, RQE and QA tasks are 81.8%, 53.2%, and 71.7%, respectively.
引用
收藏
页码:517 / 522
页数:6
相关论文
共 11 条
  • [1] Abacha A., 2017, P 26 TEXT RETRIEVAL, P15
  • [2] Abacha AB, 2019, ARXIV190108079
  • [3] Abacha Asma Ben, 2019, P BIONEP 2019 WORKSH
  • [4] Devlin J., 2018, ARXIV
  • [5] Graves A, 2012, STUD COMPUT INTELL, V385, P1, DOI [10.1162/neco.1997.9.1.1, 10.1007/978-3-642-24797-2]
  • [6] Harabagiu S, 2006, COLING/ACL 2006, VOLS 1 AND 2, PROCEEDINGS OF THE CONFERENCE, P905
  • [7] Lee J, 2019, ARXIV190108746
  • [8] Mueller J, 2016, AAAI CONF ARTIF INTE, P2786
  • [9] Rehurek R., 2010, P LREC 2010 WORKSH N, P45, DOI DOI 10.13140/2.1.2393.1847
  • [10] The probabilistic relevance framework: BM25 and beyond
    Robertson, Stephen
    Zaragoza, Hugo
    [J]. Foundations and Trends in Information Retrieval, 2009, 3 (04): : 333 - 389