# Chinese Medical Question Answer Matching Using End-to-End Character-Level Multi-Scale CNNs
## 1 Introduction
For instance, \" \" (sit)\" and \" \" \" (out of control)\" are incorrectly typed as \" \" (do)\" and \" \" \" (keep calling)\" respectively, in the question given in Table 1.  
To address the above issues, we present an end-to-end deep learning framework using character-level multi-scale convolutional neural networks (CNNs). Specifically, the framework adopts a character-level representation, viz. the character embeddings, instead of word embeddings conventionally used, so as to avoid word segmentation during preprocessing as well as its negative impacts on subsequent components. Such representations are pre-trained using the Chinese characters in both questions and answers, and similar to word embeddings, describe each character as a fixed-length vector. Since characters are less semantically meaningful compared with words in Chinese, the matching task depends more upon the capability of subsequent modules to extract relevant semantic information. This motivates us to introduce CNNs into the proposed framework, which have been proven to be good at capturing the local context of characters and words (i.e., the local interaction and dependency within n-gram). As different Chinese phrases (or words) are often different in length, we employ multi-scale CNNs (multiCNNs) to capture the interaction between them more appropriately, and hence to better \begin{table}
\begin{tabular}{l} \hline \hline
**Question**: When I get up after sitting for too long, my right foot is out of control like getting cramps! I don’t know what’s the matter! \\ \hline
**One Good Answer**: Hello: The symptom is caused by calcium deficiency and you’d better get medical tests for trace elements at the hospital. You can also take calcium tablets, such as Caltrae or calcium gluconate tablets, and eat \\ more vegetables and fruits. \\ \hline
**One Irrelevant Answer**: Hello, according to your situation, I suggest that you should \\ take cetriarine hydrochloride tablets or calcium gluconate tablets. You’d better eat less \\ irritating food, and more fruits and vegetables. Moreover, you’d better keep your skin clean, \\ and take a shower every day with sterilized bath liquid. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: An example of a question with its ground-truth answer and an irrelevant answer from the cMedQA dataset. encode the semantic association between the questions and answers. The proposed multiCNNs module consists of a stack of feature maps with different scales. To validate the proposed framework, we develop a dataset by collecting questions and answers from an online Chinese medical questions answering website ([[http://www.xywy.com](http://www.xywy.com)]([http://www.xywy.com](http://www.xywy.com))), on which questions posted by users mainly contain the description of symptoms, diagnosis and treatment of diseases, use of drugs, psychological counseling, etc., and only certified doctors are permitted to answer questions. The dataset, named cMedQA, consists of \(54,000\) questions and more than \(101,000\) answers. We made cMedQA publicly available for academic use only ([[https://github.com/zhangsheng93/cMedQA](https://github.com/zhangsheng93/cMedQA)]([https://github.com/zhangsheng93/cMedQA](https://github.com/zhangsheng93/cMedQA))). Table 1 gives an example of our data. More details of cMedQA can be found in Section 4.1. In summary, the contribution of this paper is four-fold:  
* To our best knowledge, we are the first to investigate the challenging problem of medical question answer matching in Chinese. * We propose a character-level multi-scale CNN architecture for representation learning of Chinese medical texts, which employs character embeddings to circumvent the negative influence of Chinese word segmentation, and uses multiple convolutional feature maps to extract semantic information over different scales. * We create and release the cMedQA dataset, which to the best of our knowledge is the first publicly available Chinese medical questions answer matching corpus.