# Chinese Medical Question Answer Matching Using End-to-End Character-Level Multi-Scale CNNs
## 2 Related Work
### Traditional Question Answering  
Traditional approaches to medical question answering normally involve rule-based algorithms and statistical methods with handcrafted feature sets.  
[PERSON] and [PERSON] [4] present rule-based architectures for question-answering systems in the medical domain and also discuss in detail the intricacies of rule-based question processing and answers retrieval. However, as user questions are always presented in a large number of different ways, rule-based methods may not be able to cover all such linguistic variety.  
[PERSON] et al. [5] propose an alternative approach, which first splits sentences into words to train the word vector for each sentence and then evaluates the similarity of each question-answer pair by computing the similarity between individual words. [PERSON] and [PERSON] [6] translate questions to machine-readable representations. The approach is thus able to convert a wide range of natural language questions into a standard language form. Later, the authors [7] extend their previous work by applying semantic techniques at both the representation and interrogation levels so as to create a structured query to match the entries in the knowledge base. These methods depend on manually designed patterns and handcrafted features, which often involve tremendous human labor and expertise.  
[PERSON] [8] employs a multi-label classification method and the BM25 values [9] to retrieve from a pre-built corpus multiple questions, together with their answers, that are similar to the input one.  
The resulting answers are further ranked to form a single passage using the TextRank algorithm proposed by [PERSON] and [PERSON] [10]. The proposed approach is essentially an information retrieval (IR)-based one, and the basic ideas are query expansion and retrieval results summarization.  
[PERSON] and [PERSON] [11] present a novel framework to select and rank scientific articles containing answers for clinical decision support (CDS) [12]. However, this method is likely to suffer from the lack of available external resources and the complexity of parsing differently structured texts.  
Some models about Chinese question answering have been proposed recently. [PERSON] and [PERSON] [13] constructed a Chinese question answering system named Marsha. The system recognizes known question types and formulates queries, then searches and extracts answers. [PERSON] et al. [14] proposed a method to calculate the similarity of two sentences by computing the similarity between words. [PERSON] et al. [15] constructed a semantic pattern matching model for musical domain to automatically translate questions to SPARQL queries for building the final answers. However, these methods take word segmentation as an essential step of Chinese text processing, and they do not take the inaccuracy of word segmentation toolkits into consideration, although the accuracy of word segmentation toolkit in general domain is satisfactory. [PERSON] et al. [16] proposed an approach integrating count-based and embedding-based features. They also point out in their work that character-based models outperform word-based models, which gives us a hint that dealing with Chinese characters may avoid the inaccuracy brought by word segmentation.  
To summarize, some traditional question answering methods often rely too much on manually developed rules, well-designed patterns, matching of pre-defined labels, and various external resources, which lead them to be human-labor intensive and vulnerable to concept drift frequently occurring when being applied to new datasets. In addition, some previous studies in Chinese question answering do not consider the adverse effect of word segmentation, and the adverse effect may become particularly prominent when we process domain-specific texts. As far as we know, few works have been dedicated to the key task of Chinese medical question answer matching, which is the focus of this paper.