# Chinese Medical Question Answer Matching Using End-to-End Character-Level Multi-Scale CNNs
## 1 Introduction  
Along with the rapid growth of the (mobile) Internet, more and more people in China choose to seek medical help by posting questions on some online health and wellness communities, such as DingXiangYuan ([[http://dxy.com/](http://dxy.com/)]([http://dxy.com/](http://dxy.com/))) and XunYiWenYao ([[http://www.xywy.com/](http://www.xywy.com/)]([http://www.xywy.com/](http://www.xywy.com/))). This provides great convenience for users to get medical advice from qualified doctors, as they need not go to the hospital and are able ask questions anywhere and anytime. However, a large number of users--many of whom often ask similar, if not identical, questions--have placed a tremendous burden on the doctor-side, and cause timely reply to be nearly impossible. Thus, to enhance the user experience, it is essential to develop techniques which can efficiently address the problem of medical question answer matching; namely, selecting automatically from some existing medical answer records (e.g., those posted previously by the registered doctors) the one that best matches a user's question. This paper focuses mainly on the problem of _Chinese medical question answer matching_, in which the languages of questions and answers under consideration are both limited to be Chinese. Compared with open-domain question answer matching in English studied recently by [PERSON] et al. [1] and [PERSON] et al. [2], the problem investigated in this paper is arguably more challenging due to the combination of two main factors: (1) the domain-restricted nature; and (2) some language-specific features of Chinese. The underlying challenges are further discussed as follows. First of all, due to the lack of delimiters between Chinese words, word segmentation is normally taken as an indispensable preprocessing step for many Chinese natural language processing(NLP) tasks such as part-of-speech (POS) tagging and semantic parsing, and thus considerably impacts the accuracy of those downstream tasks. Though the performance of the off-the-shelf Chinese word segmentation toolkits (e.g., ICTCLAS ([[http://ictclas.nlpir.org/](http://ictclas.nlpir.org/)]([http://ictclas.nlpir.org/](http://ictclas.nlpir.org/))), Jieba ([[https://github.com/fxsjy/jieba](https://github.com/fxsjy/jieba)]([https://github.com/fxsjy/jieba](https://github.com/fxsjy/jieba))), THULAC ([[http://thulac.thunlp.org/](http://thulac.thunlp.org/)]([http://thulac.thunlp.org/](http://thulac.thunlp.org/))), LTP ([[https://github.com/HIT-SCIR/ltp](https://github.com/HIT-SCIR/ltp)]([https://github.com/HIT-SCIR/ltp](https://github.com/HIT-SCIR/ltp)))) has reached a level satisfactory to many practical applications, they still yield errors [3], which would be inevitably propagated through the framework with a pipelined architecture and hence cause overall performance degradation. Moreover, the great variety of proper terms contained would cause further and sometimes very sharp accuracy decline to those general-purpose word segmentation tools when being directly applied to medical texts. For example, the medical terms \" \" (cetriazine hydrochloride tablets) and \" \" (calcium gluconate tablets) in Table 1 are incorrectly segmented into \" \" \" \" and \" \" \" \" by the Jieba segmentation toolkit ([[https://github.com/fxsjy/jieba](https://github.com/fxsjy/jieba)]([https://github.com/fxsjy/jieba](https://github.com/fxsjy/jieba))). Although the introduction of domain-specific lexicons can mitigate the negative effect of professional terminology on word segmentation, building such lexicons always seems prohibitive, as it involves a large amount of manual labor and requires a great deal of domain-specific knowledge and expertise. Even worse is that pre-defined lexicons tend to be inappropriate when coping with the user-composed and unedited medical questions and answers posted on online communities, as they are written in an informal style and often contain many short-hand notations, non-standard acronyms, and even typos and ungrammatical sentences.