<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="tawfik-spruit-2019-uu">
    <titleInfo>
        <title>UU_TAILS at MEDIQA 2019: Learning Textual Entailment in the Medical Domain</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Noha</namePart>
        <namePart type="family">Tawfik</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Marco</namePart>
        <namePart type="family">Spruit</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2019-08</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 18th BioNLP Workshop and Shared Task</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Dina</namePart>
            <namePart type="family">Demner-Fushman</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Kevin</namePart>
            <namePart type="given">Bretonnel</namePart>
            <namePart type="family">Cohen</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Sophia</namePart>
            <namePart type="family">Ananiadou</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Junichi</namePart>
            <namePart type="family">Tsujii</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Florence, Italy</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>This article describes the participation of the UU_TAILS team in the 2019 MEDIQA challenge intended to improve domain-specific models in medical and clinical NLP. The challenge consists of 3 tasks: medical language inference (NLI), recognizing textual entailment (RQE) and question answering (QA). Our team participated in tasks 1 and 2 and our best runs achieved a performance accuracy of 0.852 and 0.584 respectively for the test sets. The models proposed for task 1 relied on BERT embeddings and different ensemble techniques. For the RQE task, we trained a traditional multilayer perceptron network based on embeddings generated by the universal sentence encoder.</abstract>
    <identifier type="citekey">tawfik-spruit-2019-uu</identifier>
    <identifier type="doi">10.18653/v1/W19-5053</identifier>
    <location>
        <url>https://aclanthology.org/W19-5053</url>
    </location>
    <part>
        <date>2019-08</date>
        <extent unit="page">
            <start>493</start>
            <end>499</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T UU_TAILS at MEDIQA 2019: Learning Textual Entailment in the Medical Domain
%A Tawfik, Noha
%A Spruit, Marco
%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 tawfik-spruit-2019-uu
%X This article describes the participation of the UU_TAILS team in the 2019 MEDIQA challenge intended to improve domain-specific models in medical and clinical NLP. The challenge consists of 3 tasks: medical language inference (NLI), recognizing textual entailment (RQE) and question answering (QA). Our team participated in tasks 1 and 2 and our best runs achieved a performance accuracy of 0.852 and 0.584 respectively for the test sets. The models proposed for task 1 relied on BERT embeddings and different ensemble techniques. For the RQE task, we trained a traditional multilayer perceptron network based on embeddings generated by the universal sentence encoder.
%R 10.18653/v1/W19-5053
%U https://aclanthology.org/W19-5053
%U https://doi.org/10.18653/v1/W19-5053
%P 493-499

Markdown (Informal)

[UU_TAILS at MEDIQA 2019: Learning Textual Entailment in the Medical Domain](https://aclanthology.org/W19-5053) (Tawfik & Spruit, BioNLP 2019)

• UU_TAILS at MEDIQA 2019: Learning Textual Entailment in the Medical Domain (Tawfik & Spruit, BioNLP 2019)

ACL

• Noha Tawfik and Marco Spruit. 2019. UU_TAILS at MEDIQA 2019: Learning Textual Entailment in the Medical Domain. In Proceedings of the 18th BioNLP Workshop and Shared Task, pages 493–499, Florence, Italy. Association for Computational Linguistics.