ARS_NITK at MEDIQA 2019:Analysing Various Methods for Natural Language Inference, Recognising Question Entailment and Medical Question Answering System

Agrawal, Anumeha; George, Rosa Anil; Ravi, Selvan Suntiha; S, Sowmya Kamath; Kumar, Anand

doi:10.18653/v1/w19-5059

Cited by 12 publications

(9 citation statements)

References 18 publications

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“…Task(s) ANU-CSIRO (Nguyen et al, 2019) NLI, RQE, QA ARS NITK (Agrawal et al, 2019) NLI, RQE, QA DoubleTransfer (Xu et al, 2019) NLI, RQE, QA Dr.Quad (Bannihatti Kumar et al, 2019) NLI, RQE, QA DUT-BIM (Zhou et al, 2019a) QA DUT-NLP (Zhou et al, 2019b) RQE, QA IITP (Bandyopadhyay et al, 2019) NLI, RQE, QA IIT-KGP (Sharma and Roychowdhury, 2019) RQE KU ai (Cengiz et al, 2019) NLI lasigeBioTM (Lamurias and Couto, 2019) NLI, RQE, QA MSIT SRIB (Chopra et al, 2019) NLI NCUEE (Lee et al, 2019b) NLI PANLP (Zhu et al, 2019) NLI, RQE, QA Pentagon (Pugaliya et al, 2019) NLI, RQE, QA Saama Research (Kanakarajan, 2019) NLI Sieg (Bhaskar et al, 2019) NLI, RQE Surf (Nam et al, 2019) NLI UU TAILS (Tawfik and Spruit, 2019) NLI, RQE UW-BHI (Kearns et al, 2019) NLI WTMED (Wu et al, 2019) NLI which builds up on BERT to perform multi-task learning and is evaluated on the GLUE benchmark (Wang et al, 2018). A common theme across all the papers was training of multiple models and then using an ensemble as the final system which performed better than the individual models.…”

Section: Teammentioning

confidence: 99%

Overview of the MEDIQA 2019 Shared Task on Textual Inference, Question Entailment and Question Answering

Abacha¹,

Shivade²,

Demner‐Fushman³

2019

Proceedings of the 18th BioNLP Workshop and Shared Task

109

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This paper presents the MEDIQA 2019 shared task organized at the ACL-BioNLP workshop. The shared task is motivated by a need to develop relevant methods, techniques and gold standards for inference and entailment in the medical domain, and their application to improve domain specific information retrieval and question answering systems. MEDIQA 2019 includes three tasks: Natural Language Inference (NLI), Recognizing Question Entailment (RQE), and Question Answering (QA) in the medical domain. 72 teams participated in the challenge, achieving an accuracy of 98% in the NLI task, 74.9% in the RQE task, and 78.3% in the QA task. In this paper, we describe the tasks, the datasets, and the participants' approaches and results. We hope that this shared task will attract further research efforts in textual inference, question entailment, and question answering in the medical domain. 12 metamap.nlm.nih.gov 13 github.com/abachaa/MedQuAD 14 github.com/Team-IIT-KGP/Qspider

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Section: Teammentioning

confidence: 99%

Overview of the MEDIQA 2019 Shared Task on Textual Inference, Question Entailment and Question Answering

Abacha¹,

Shivade²,

Demner‐Fushman³

2019

Proceedings of the 18th BioNLP Workshop and Shared Task

109

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“…The transbronchial biopsy was nondiagnostic. Patient has a mediastinal mass entailment neutral (Wu et al, 2019;Zhu et al, 2019;Xu et al, 2019;Bhaskar et al, 2019;Agrawal et al, 2019;Pugaliya et al, 2019;Bannihatti Kumar et al, 2019;Tawfik and Spruit, 2019;Cengiz et al, 2019), respectively.…”

Section: History Of Heart Attack Entailment Neutralmentioning

confidence: 99%

Surf at MEDIQA 2019: Improving Performance of Natural Language Inference in the Clinical Domain by Adopting Pre-trained Language Model

Nam

Yoon

Jung

2019

Proceedings of the 18th BioNLP Workshop and Shared Task

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While deep learning techniques have shown promising results in many natural language processing (NLP) tasks, it has not been widely applied to the clinical domain. The lack of large datasets and the pervasive use of domain-specific language (i.e. abbreviations and acronyms) in the clinical domain causes slower progress in NLP tasks than that of the general NLP tasks. To fill this gap, we employ word/subword-level based models that adopt large-scale data-driven methods such as pretrained language models and transfer learning in analyzing text for the clinical domain. Empirical results demonstrate the superiority of the proposed methods by achieving 90.6% accuracy in medical domain natural language inference task. Furthermore, we inspect the independent strengths of the proposed approaches in quantitative and qualitative manners. This analysis will help researchers to select necessary components in building models for the medical domain. 1 https://allennlp.org/elmo

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“…This is particularly important for medical conversational agents (Wu et al, 2020), as Consumer Health Questions (CHQ) are often long and contain peripheral information not needed to answer the question. Approaches to medical question understanding include query relaxation (Ben Abacha and Zweigenbaum, 2015;Lei et al, 2020), question entailment recognition Demner-Fushman, 2016, 2019b;Agrawal et al, 2019) and summarization (Ben Abacha and Demner-Fushman, 2019a).…”

Section: Introductionmentioning

confidence: 99%

Joint Summarization-Entailment Optimization for Consumer Health Question Understanding

Mrini¹,

Dernoncourt²,

Chang³

et al. 2021

Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations

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Understanding the intent of medical questions asked by patients, or Consumer Health Questions, is an essential skill for medical Conversational AI systems. We propose a novel dataaugmented and simple joint learning approach combining question summarization and Recognizing Question Entailment (RQE) in the medical domain. Our data augmentation approach enables to use just one dataset for joint learning. We show improvements on both tasks across four biomedical datasets in accuracy (+8%), ROUGE-1 (+2.5%) and human evaluation scores. Human evaluation shows joint learning generates faithful and informative summaries. Finally, we release our code, the two question summarization datasets extracted from a large-scale medical dialogue dataset, as well as our augmented datasets 1 .

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ARS_NITK at MEDIQA 2019:Analysing Various Methods for Natural Language Inference, Recognising Question Entailment and Medical Question Answering System

Cited by 12 publications

References 18 publications

Overview of the MEDIQA 2019 Shared Task on Textual Inference, Question Entailment and Question Answering

Overview of the MEDIQA 2019 Shared Task on Textual Inference, Question Entailment and Question Answering

Surf at MEDIQA 2019: Improving Performance of Natural Language Inference in the Clinical Domain by Adopting Pre-trained Language Model

Joint Summarization-Entailment Optimization for Consumer Health Question Understanding

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