## 2 Related Work  
Traditionally, QA tasks have been designed to be text-dependent [4; 6; 18], where a model is required to comprehend a given text to answer questions. Those given texts relevant to the questions are specially curated by people, which is infeasible for real-world applications where annotations of relevant context are expensive to obtain. This gave rise to the birth of open-domain QA (OpenQA) task, where models must both find and comprehend the context for answering the questions. As a preliminary trial, [PERSON] et al. [10] proposes the DrQA model, which uses a text retriever to obtain relevant documents from Wikipedia, and further applies a trained reading comprehension model to extract the answer from the retrieved documents. Afterwards, researchers have introduced more sophisticated models, which either aggregate all informative evidence [11; 12] or filter out those irrelevant retrieved texts [19; 20] to better predict the answers. Benefiting from the power of neural networks, these models have achieved remarkable progress in OpenQA.  
Much of the previous OpenQA work focuses on the datasets whose answers are spans from the retrieved documents [9; 10; 14]. In general, most questions concern the facts that are explicitly expressed in the text, offering an advantage to systems that rely mostly on surface word matching [21].  
To promote more advanced reading skills, another research line has studied OpenQA tasks in a free-form multiple-choice form [16; 17]. These benchmarks allow a more comprehensive evaluation of different higher-level reading skills such as logical reasoning and prior knowledge integration. In particular, real-world exams such as SAT and Gaokao are ideal sources for constructing this kind of OpenQA datasets [16; 17; 22]. Our proposed dataset follows this line but differs in three aspects:  
* The source of our dataset is designed to examine the doctors' professional capability and thus contains a significant number of questions that require multi-hop logical reasoning, which helps push the development of reading comprehension models along this direction [23].
* Our dataset is the first publicly available large-scale multiple-choice OpenQA dataset for the medical problems, where extensive prior domain-specific knowledge is anticipated for the model. It can thus contribute to the emerging field where a general language model will need to be combined with world knowledge.
* Our dataset is cross-lingual, covering English and simplified/traditional Chinese, which contributes to the emerging field of cross-lingual natural language understanding.  
There are several related medical QA datasets, which are summarized in Table 2. Among them, LiveQA [24], Medication QA [25], MEDIQA [26], and MedQuAD [27] contain consumer health related questions and the answers are obtained by searching healthcare websites such as MedlinePlus via the ChiQA system [28] and selecting a snippet of searching results out, where the answer retrieval is performed by keywords matching and complex reasoning is seldom involved. BioASQ [29] is similar to the SQuAQ dataset [4], where a span of text in the given context is used as the answer and thus no external knowledge source is needed. emrQA [30] aims to rank a list of Electronic Medical Record (EMR) text lines to find the best line as the answer and the ground truth answers are obtained via an algorithm rather than human annotation. Overall, none of these related datasets have been formulated as an OpenQA problem. Last but not least, [PERSON] et al. [22] and [PERSON] and [PERSON] [31] have previously worked on the Chinese and English versions of our proposed dataset, respectively. However, the former did not release any data while the latter only released 454 questions for public use, while we publicize a large-scale dataset to promote more powerful deep models.