# Automatic Essay Evaluation Technologies in Chinese Writing--A Systematic Literature Review
## 3 Results
### Scoring Model (RQ3)
#### 3.3.2 Deep Learning-Based Scoring Model  
The deep learning methods based on DNN can bypass the feature engineering stage by automatically extracting features for essay scoring through hidden layers. These scoring models employ text embedding as typical input and continuously update feature values through training on a large corpus to achieve optimal predictive results. Deep learning models generally require a large-scale training corpus and lack interpretability in feature extraction, making it challenging to support feedback and recommendation generation. To address these issues, researchers have employed deep learning methods such as transfer learning, multi-task learning, and fusion of feature engineering to construct scoring models. (1) Transfer learning  
The performance of the scoring model can be affected by variations in writing style, topic, and the author's grade level. Therefore, ACEE research primarily adopts the following two methods to enhance the model's adaptability. The first method, known as prompt-free or prompt-independent, constructs evaluation models by selecting feature sets that are not affected by thematic factors from multi-topic corpora. However, due to the absence of topic-related features, this method may result in high biases in some scores. [PERSON], [PERSON] et al. [35] found that the scoring system would overestimate the scores for essays demonstrating outstanding language proficiency while lacking relevance to the given topic. The second method enables the scoring model to adapt to grade levels and topic ratings by employing transfer learning. [PERSON], [PERSON] et al. [60] improved the performance of the DNN model in cross-topic scoring through three stages: pre-training using weakly supervised data and cross-topic data, fine-tuning using target-topic data which needed less than 1000 training essays for one prompt. [PERSON], [PERSON] et al. [45] designed a general scoring model calibration scheme that can adapt to grades and themes. Firstly, a general scoring model was trained using a universal scoring dataset disregarding grade levels and topics. Then, the independent fully connected regression score layers were used to train grade-specific general scoring models for grade-level scoring tasks using essays from different grade levels. Finally, a Bayesian ridge regression model was trained using a small target-topic essay dataset to predict scores for specific-topic essays. (2) Multi-task Learning  
The pre-trained embedded model needs sufficient data for fine-tuning in ACEE scoring tasks. Still, the fine-tuning effect cannot be guaranteed if the target topic corpus is insufficient. To address this issue, [PERSON], [PERSON] et al. [34] employed a multi-task learning approach by incorporating two auxiliary tasks, namely topic prediction and topic matching, into the scoring task. The model learns a shared representation or feature extractor across these tasks by sharing parameters. This method facilitates the model's ability to capture task-specific patterns and shared underlying structures, improving generalization and efficiency, so it effectively resolves multi-topic corpora-trained models' performance limitations when scoring essays on specific topics. (3) Hybrid Features  
Traditional scoring methods based on feature engineering and feature-free deep learning methods have advantages and limitations. However, a hybrid scoring model that combines both methods can achieve both accuracy and interpretability in scoring. In their work, [PERSON], [PERSON] et al. [47] extracted 35 commonly used statistical features and embedded semantic features represented by LSI using Coh-Metrix. A CNN model was then built to predict scores based on these features to predictive scores. [PERSON], [PERSON] et al. [45] employed various NLP methods to extract shallow statistical features and deep features related to language usage, language expression, discourse anomalies, and text quality. These features were combined with text embedding features obtained through pre-trained BERT.