## 1 Introduction  
During the writing stage, examinees are asked to write an essay according to the prompt; then, scorers mark the essay. Since the scoring requires a lot of time and effort, it is difficult to grade a large number of essays [1]. In addition, the scorers are easily influenced by personal subjective factors in the grading process [2]. AES is a technique that utilizes NLP technology to evaluate essays, which can be analyzed and evaluated through multiple aspects (such as language, structure, and content) with an objective, fast, and accurate scoring result [3]. AES can circumvent many disadvantages of traditional scoring methods, save labor costs, and not be influenced by personal subjective factors [4], while substantially improving the fairness and accuracy of scoring. Over the years, we have designed many AES methods, which can generally be classified as feature engineering methods or DNN methods [5]. Early feature engineering-based AES methods construct shallow features based on scoring criteria, such as grammar, syntax, and chapter structure; then, they use machine learning to indirectly evaluate the essay [6]. Such technology, which is based on hand-crafted features, overlooks the potential deep information in the essay. Consequently, it cannot obtain satisfactory results for AES tasks, which require the use of the deep semantic information in the essay. Secondly, this technology requires a lot of time and cost in terms of hand-crafted feature extraction. In addition, the evaluation criteria of different AES tasksare different, thus resulting in the weak generalization ability of the above methods in realistic scenarios. Recently, DNN approaches have obtained outstanding performance in AES tasks. DNN-AES models are trained using large-scale datasets to score essays based on a word or sentence embedding representation [7]. These methods extract high-dimensional features in essays through the extraction, crossover, and fusion of neural units. However, the scoring process is a complex human activity that requires different levels to score essays; a single network structure cannot fully capture the semantic features of essays. As a result, the performance of a DNN-only approach is somewhat limited in the scoring process. Some researchers recently point out that the hybrid model fusing different scales of essays can significantly enhance the scoring capability [8; 9]. Specifically, the document-scale feature can characterize the global information, and the sentence-scale feature can extract the local information in the essays that is lost in document-scale features. To obtain the advantages of the feature engineering and DNN method, [PERSON] et al. [10] proposed a hybrid approach. It is a DNN-AES model with features formulated with the document-scale features and an additional recurrent neural network (RNN) for processing the sentence-scale manual features. This hybrid approach obtains advanced accuracy but has three drawbacks. First, it manually extracts sentence-scale features and cannot be fine-tuned according to the specific tasks, which results in a suboptimal performance and makes it difficult to extend to other AES tasks of different datasets. Secondly, it does not incorporate the shallow information in essays via the shallow linguistic feature extraction used in previous research. Thirdly, it does not take into account the relevance between the essay and the corresponding prompt, which will be considered in scoring. * In order to reduce the influences of the above problems and to score essays more comprehensively, we propose the AES method based on Multi-Scale Semantic Features (MSSF). In particular, we extract multiple-scale characteristics with different modules: (1) We utilize the LSTM-MoT model to extract document-scale global semantic features of essays. (2) After the sentence vector of the essay is extracted by the SBERT, the context relevance of the local features is extracted by LSTM.