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.
@inproceedings{tawfik-spruit-2019-uu, title = "{UU}{\_}{TAILS} at {MEDIQA} 2019: Learning Textual Entailment in the Medical Domain", author = "Tawfik, Noha and Spruit, Marco", 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/W19-5053/", doi = "10.18653/v1/W19-5053", pages = "493--499", 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."}
<?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