@inproceedings{bandyopadhyay-etal-2019-iitp,
title = "{IITP} at {MEDIQA} 2019: Systems Report for Natural Language Inference, Question Entailment and Question Answering",
author = "Bandyopadhyay, Dibyanayan and
Gain, Baban and
Saikh, Tanik and
Ekbal, Asif",
editor = "Demner-Fushman, Dina and
Cohen, Kevin Bretonnel and
Ananiadou, Sophia and
Tsujii, Junichi",
booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-5056",
doi = "10.18653/v1/W19-5056",
pages = "517--522",
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 the 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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<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 the three tasks. The highest performance obtained in NLI, RQE and QA tasks are 81.8%, 53.2%, and 71.7%, respectively.</abstract>
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%0 Conference Proceedings
%T IITP at MEDIQA 2019: Systems Report for Natural Language Inference, Question Entailment and Question Answering
%A Bandyopadhyay, Dibyanayan
%A Gain, Baban
%A Saikh, Tanik
%A Ekbal, Asif
%Y Demner-Fushman, Dina
%Y Cohen, Kevin Bretonnel
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S Proceedings of the 18th BioNLP Workshop and Shared Task
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F bandyopadhyay-etal-2019-iitp
%X 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.
%R 10.18653/v1/W19-5056
%U https://aclanthology.org/W19-5056
%U https://doi.org/10.18653/v1/W19-5056
%P 517-522
Markdown (Informal)
[IITP at MEDIQA 2019: Systems Report for Natural Language Inference, Question Entailment and Question Answering](https://aclanthology.org/W19-5056) (Bandyopadhyay et al., BioNLP 2019)
ACL