Generating Explanations in Medical Question-Answering by Expectation
  Maximization Inference over Evidence
Wei Sun Mingxiao Li Damien Sileo Jesse Davis Marie-Francine Moens
Medical Question Answering~(medical QA) systems play an essential role in
assisting healthcare workers in finding answers to their questions. However, it
is not sufficient to merely provide answers by medical QA systems because users
might want explanations, that is, more analytic statements in natural language
that describe the elements and context that support the answer. To do so, we
propose a novel approach for generating natural language explanations for
answers predicted by medical QA systems. As high-quality medical explanations
require additional medical knowledge, so that our system extract knowledge from
medical textbooks to enhance the quality of explanations during the explanation
generation process. Concretely, we designed an expectation-maximization
approach that makes inferences about the evidence found in these texts,
offering an efficient way to focus attention on lengthy evidence passages.
Experimental results, conducted on two datasets MQAE-diag and MQAE, demonstrate
the effectiveness of our framework for reasoning with textual evidence. Our
approach outperforms state-of-the-art models, achieving a significant
improvement of \textbf{6.86} and \textbf{9.43} percentage points on the Rouge-1
score; \textbf{8.23} and \textbf{7.82} percentage points on the Bleu-4 score on
the respective datasets.