Evaluating recognizing question entailment methods for a Portuguese Community Question-Answering System about Diabetes Mellitus

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dc.creatorThiago Castro Ferreira
dc.creatorJoão Victor de Pinho Costa
dc.creatorDaniel Hasan Dalip
dc.creatorCelso França
dc.creatorMarcos André Gonçalves
dc.creatorRodrigo Bastos Fóscolo
dc.creatorAdriana Silvina Pagano
dc.creatorIsabela Rigotto
dc.creatorVitoria Portella
dc.creatorGabriel Frota
dc.creatorAna Luisa A. R. Guimarães
dc.creatorAdalberto Penna
dc.creatorIsabela Lee
dc.creatorTayane A. Soares
dc.creatorSophia Rolim
dc.creatorRossana Cunha
dc.creatorAriel Santos
dc.creatorRivaney F. Oliveira
dc.creatorAbisague Langbehn
dc.date.accessioned2023-08-04T20:03:10Z
dc.date.accessioned2025-09-08T23:54:34Z
dc.date.available2023-08-04T20:03:10Z
dc.date.issued2021
dc.description.sponsorshipCNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico
dc.description.sponsorshipFAPEMIG - Fundação de Amparo à Pesquisa do Estado de Minas Gerais
dc.description.sponsorshipCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
dc.format.mimetypepdf
dc.identifier.doihttps://doi.org/10.26615/978-954-452-072-4_028
dc.identifier.isbn9789544520724
dc.identifier.urihttps://hdl.handle.net/1843/57494
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofInternational Conference Recent Advances in Natural Language Processing
dc.rightsAcesso Aberto
dc.subjectLingüística
dc.subjectCiência da Computação
dc.subjectDiabetes
dc.titleEvaluating recognizing question entailment methods for a Portuguese Community Question-Answering System about Diabetes Mellitus
dc.typeArtigo de evento
local.citation.epage243
local.citation.issue2021
local.citation.spage234
local.description.resumoThis study describes the development of a Portuguese Community-Question Answering benchmark in the domain of Diabetes Mellitus using a Recognizing Question Entailment (RQE) approach. Given a premise question, RQE aims to retrieve semantically similar, already answered, archived questions. We build a new Portuguese benchmark corpus with 785 pairs between premise questions and archived answered questions marked with relevance judgments by medical experts. Based on the benchmark corpus, we leveraged and evaluated several RQE approaches ranging from traditional information retrieval methods to novel large pre-trained language models and ensemble techniques using learn-to-rank approaches. Our experimental results show that a supervised transformer-based method trained with multiple languages and for multiple tasks (MUSE) outperforms the alternatives. Our results also show that ensembles of methods (stacking) as well as a traditional (light) information retrieval method (BM25) can produce competitive results. Finally, among the tested strategies, those that exploit only the question (not the answer), provide the best effectiveness-efficiency trade-off. Code is publicly available.
local.identifier.orcidhttps://orcid.org/0000-0003-0200-3646
local.identifier.orcidhttps://orcid.org/0000-0002-8532-7701
local.identifier.orcidhttps://orcid.org/0000-0002-3150-3503
local.identifier.orcidhttps://orcid.org/0000-0002-0251-7172
local.identifier.orcidhttps://orcid.org/0000-0002-2075-3363
local.identifier.orcidhttps://orcid.org/0000-0001-5403-8360
local.identifier.orcidhttps://orcid.org/0000-0002-8548-7625
local.identifier.orcidhttps://orcid.org/0000-0003-4440-8123
local.publisher.countryBrasil
local.publisher.departmentFALE - FACULDADE DE LETRAS
local.publisher.departmentICX - DEPARTAMENTO DE CIÊNCIA DA COMPUTAÇÃO
local.publisher.departmentMED - DEPARTAMENTO DE CLÍNICA MÉDICA
local.publisher.initialsUFMG

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