Use este identificador para citar ou linkar para este item: http://hdl.handle.net/1843/57494
Tipo: Artigo de Evento
Título: Evaluating recognizing question entailment methods for a Portuguese Community Question-Answering System about Diabetes Mellitus
Autor(es): Thiago Castro Ferreira
João Victor de Pinho Costa
Daniel Hasan Dalip
Celso França
Marcos André Gonçalves
Rodrigo Bastos Fóscolo
Adriana Silvina Pagano
Isabela Rigotto
Vitoria Portella
Gabriel Frota
Ana Luisa A. R. Guimarães
Adalberto Penna
Isabela Lee
Tayane A. Soares
Sophia Rolim
Rossana Cunha
Ariel Santos
Rivaney F. Oliveira
Abisague Langbehn
Resumo: This 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.
Assunto: Lingüística
Ciência da Computação
Diabetes
Idioma: eng
País: Brasil
Editor: Universidade Federal de Minas Gerais
Sigla da Instituição: UFMG
Departamento: FALE - FACULDADE DE LETRAS
ICX - DEPARTAMENTO DE CIÊNCIA DA COMPUTAÇÃO
MED - DEPARTAMENTO DE CLÍNICA MÉDICA
Tipo de Acesso: Acesso Aberto
Identificador DOI: https://doi.org/10.26615/978-954-452-072-4_028
URI: http://hdl.handle.net/1843/57494
Data do documento: 2021
metadata.dc.relation.ispartof: International Conference Recent Advances in Natural Language Processing
Aparece nas coleções:Artigo de Evento



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