A Gradually Soft Multi-Task and Data-Augmented Approach to Medical Question Understanding (2021)

Abstract: Users of medical question answering systems often submit long and detailed questions, making it hard to achieve high recall in answer retrieval. To alleviate this problem, we propose a novel Multi-Task Learning (MTL) method with data augmentation for medical question understanding. We first establish an equivalence between the tasks of question summarization and Recognizing Question Entailment (RQE) using their definitions in the medical domain. Based on this equivalence, we propose a data augmentation algorithm to use just one dataset to optimize for both tasks, with a weighted MTL loss. We introduce gradually soft parameter-sharing: a constraint for decoder parameters to be close, that is gradually loosened as we move to the highest layer. We show through ablation studies that our proposed novelties improve performance. Our method outperforms existing MTL methods across 4 datasets of medical question pairs, in ROUGE scores, RQE accuracy and human evaluation. Finally, we show that our method fares better than single-task learning under 4 low-resource settings.

## introduction in order to retrieve relevant answers, one of the basic steps in question answering (qa) systems is understanding the intent of questions @xcite . this is particularly important for medical qa systems @xcite , as consumer health questions -questions asked by patients -may use a vocabulary distinct from doctors to describe similar health concepts @xcite . consumer health questions may also contain peripheral information like patient history @xcite , that are not necessary to answer questions. there is a growing number of approaches to medical question understanding, including query relax-source user-written question or consumer health question (chq): subject: morgellon disease. message: it appears as if i have had this horrible disease for many, many years and it is getting worst. i am trying to find a physician or specialist in the south carolina area who can treat me for this medical/mental disease. it seems as if this disease has "no" complete treatment and it is more least a disability! ## methodology we consider the multi-task learning of medical question summarization and medical rqe. the input to both tasks is a pair of medical questions. the first question is called a consumer health question (chq), and the second question is called a frequently asked question (faq). the chq is written by a patient and is usually longer and more informal, whereas the faq is usually a singlesentence question written by a medical expert. the purpose of both tasks is to match a chq to an faq, and ultimately to an expert-written answer that matches the faq. an example pair is shown in figure 1 . our novel gradually soft multi-task and dataaugmented learning approach to medical question understanding has four main components. first, we establish the equivalence between medical question pairs in question summarization and rqe. then, we use our equivalence observation to propose a scheme for data augmentation. third, we show our simultaneous multi-task learning model architecture and learning objective. finally, we describe our gradually soft parameter-sharing scheme. ## conclusions we propose a novel multi-task learning approach for medical question understanding. our approach trains on the tasks of rqe and question summarization in a simultaneous, weighted mtl loss function, where we add a loss term to constrain the decoder layers to be close, and we loosen the constraint gradually as we move higher up the layers. we show using the definitions of both tasks in the medical domain that we can augment datasets, such that we only need one dataset for mtl. our two ablation studies show that our gradually soft parameter-sharing and our data augmentation algorithm each increase performance individually. we compare our method to single-task learning and existing mtl work, and show improvements across 3 medical question summarization datasets and 1 medical rqe dataset. finally, we test our approach under low-resource settings: we find that it is able to efficiently leverage small quantities of data, and that these performance increases do not only depend on additional data from augmentation.