Evaluating Recognizing Question Entailment Methods for a Portuguese Community Question-Answering System about Diabetes Mellitus

RANLP 2021 · Thiago castro Ferreira, João Victor de Pinho Costa, Isabela Rigotto, Vitoria Portella, Gabriel Frota, Ana Luisa A. R. Guimarães, Adalberto Penna, Isabela Lee, Tayane A. Soares, Sophia Rolim, Rossana Cunha, Celso França, Ariel Santos, Rivaney F. Oliveira, Abisague Langbehn, Daniel Hasan Dalip, Marcos André Gonçalves, Rodrigo Bastos Fóscolo, Adriana Pagano ·

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.