## 1 Introduction  
In this paper, we apply a modified neural architecture search (NAS) proposed in [1; 2; 3; 4; 5; 6] to a grammaticality task for Korean linguistic phenomena. To our knowledge, it is a novel approach to language modeling of Korean linguistic phenomena in terms of automatic neural architecture design. The successful application of deep learning in various fields is due to its automation of pattern finding and powerful performance on difficult problems [7; 8]. The fields include image recognition [9; 10], natural language processing (NLP) [11; 12], game artificial intelligence (AI) [13], self-driving system [14; 15], and agriculture [16], among many others. In particular, the deep learning methods have been applied to the fields of psycholinguistics, which attempt to identify the cognitive processing of human languages [17]. This success requires a need for architecture engineering, where more complex neural architectures are designed manually for different tasks. Although open-source toolkits for deep learning have become more various and easy to use [18], the diversity of tasks raises the need for the automation of architecture engineering. NAS, which aims to automate architecture engineering, focuses on automation of engineering architectures, and NAS methods have shown successful results in various tasks including image classification [19; 20], object detection [21], or semantic segmentation [22]. NAS can be treated as a subfield of autoML [1] and significantly overlaps with hyper-parameter optimization [23]. Hyper-parameter optimization (HPO) mainly uses Bayesian optimization; however, it cannot generate the topology of neural architecture. The purpose of Baysian optimization is not on the generation of a topology. By its design, this focuses on parameter optimization of a neural architecture. The optimized neural architecture by the HPO would not change the final topology of the original neural network. The autoML thus contains two steps: the first step is to generate topology by using NAS; the second step is to optimize the hyperparameter of the neural network created by NAS. To generate topology, we need to adopt an evolutionary algorithm. While there are many different kinds of evolutionary algorithms available, the algorithms in question are required to begin from the minimum structure. Here we adopt the variable chromosome genetic algorithm [6], which does not require the minimum structure. This translates into a more efficient tuning method for existing deep learning models to achieve a higher rate of accuracy, as we can start directly from the previous status into a higher accuracy model. In line with this growing interest in automated architecture engineering, we apply the NAS method to word order patterns found in Korean. Linearization is a process of generating grammatical word orders for a given set of words. Syntactic linearization systems refer to the process of making the grammatical word orders with their syntactic trees. Various word order patterns have been accounted for under syntactic linearization [24; 25]. Recently, various language models have been widely tested with various types of word ordering tasks including sentence corrections and word predictions using sequence-to-sequence approaches. They have been compared in terms of their accuracy, and the literature has proven successful on the tasks even without any explicit syntactic information [26]. The Korean language poses an interesting challenge in word order patterns due to two linguistic phenomena: (i) scrambling [4], which permits different ordering of elements, as shown in (1); and (ii) argument ellipsis [5], which permits the missing argument as shown in (2) (the missing argument is crossed in the example). These two properties add complexity into word order patterns, which only exhibit a resulting linear word order that contains underlying linguistic operations. 1. a. [PERSON] coahanta. John-subject Mary-object like [PERSON] likes [PERSON]. b. [PERSON] coahanta. Mary-object John-subject '[PERSON] likes [PERSON].'