1. Introduction
The most important part of descriptive scoring is determining whether the scoring is consistent based on valid criteria [1]. Therefore, if these scoring criteria and processes can be automated, scoring costs and the temporal burdens of large-scale evaluations could be greatly reduced. Additionally, learners could improve their writing skills faster by receiving immediate feedback on their written inputs. To this end, it is necessary to determine whether automatic scoring is possible for Korean learners’ descriptive answers containing various errors of form, syntax, and grammar. Research related to automatic scoring capabilities for descriptive Korean-language text-exam responses has developed alongside corresponding natural language processing (NLP)-related technologies [2-4]. A language model performs the allocation and probability determination of word sequences and sentences in NLP [5]. Machine- and deep-learning technologies, such as the embeddings-from-language model, the generative pre-training model, and the bidirectional encoder representations from transformers (BERT), have been developed for NLP tasks [6,7]. Unfortunately, their application to automatic scoring systems is lacking [8].
In this paper, a pre-trained BERT model is used to score foreign Korean-language students’ descriptive written test answers, and the results are compared to other language model techniques. BERT’s pre-trained model can provide consistent results when trained on a small number of data [6]. Fig. 1 displays the high-level BERT model.
2. Characteristics of Descriptive Answers of Korean Learners
2.1 Intermediate Korean Learner’s Language Characteristics
There are 200 training hours for each grade [TeX:] $$(10 \text { weeks } \times 20 \text { hours })$$ according to the International Korean Curriculum Standard for regular domestic institutions and the Korean Language Proficiency Test, governed by the National Institute of Korean Language. The data used in this paper correspond to Intermediate 1 learners, whose goals include being able to write abstract topics about familiar social life in a simple structure [9]. At the intermediate level, learners have accomplished 350–400 hours of Korean language learning and have acquired about 100 grammar knowledge items and 1,000 vocabulary words.
These learners can create complete sentences, but there are often errors in word order. Complexity increases when developing various sentence structures related to life experiences. Basic errors that often appear are related to the persistence of the grammar systems applied in their native language. Students have a strong tendency to ask teachers for detailed explanations to help them reconcile the new rules to their old rules. However, in terms of vocabulary learning, students are interested in various media sources for learning and immersion. Furthermore, learners who are not familiar with Chinese characters suddenly realize the need to learn Chinese characters. Many structural vocabulary errors have been catalogued, including errors of derivation, behavior, and instruction [10].
In this study, data are obtained from past processes of teaching Korean to foreign students, where the foreign students are learning Korean. These students must acquire and master morphological, syntactic, and pragmatic language rules for the first time. The focus of this research is not on analyzing the quality of prose. Instead, it is about analyzing language rules in the context of midterm and final exams. This type of writing consists of personal introductions and simple expository writings.
2.2 Test Question Detail
The test questions for intermediate-level learners are not based on proficiency evaluations but on the curricula of educational institutions. Most learners score more than 70 points. For example, a test question may direct the student to write more than 500 characters about a memorable travel account and provide categorical details, inclusively, of destination, transportation, purpose, sights, feelings, events, and reasons using complete sentences.
3. A BERT-Based Automatic Scoring Model
3.1 Model Design
A BERT-Based Automatic Scoring Model consists of three modules: data analysis, data preprocessing, and data learning (Fig. 2).
A BERT-Based Automatic Scoring Model.
Because the actual tests were conducted as written exams in which answers were written on paper, the contents of the answers were entered as a text file that included all typos, spaces, non-grammatical items, and symbols as-written by learners. A tag of “unknown” was added if the writing could not be recognized. Items were analyzed based on the English special character ratio, the distribution of the answer length, and the graded answer data. Factors, such as special characters and numbers, are parts that interfere with learning. The lengths of the answers and the distribution of scores were correlated to the grades.
In the BERT’s preprocessing module, sentence tokenization was required. A [CLS] indicator was attached to the beginning of the text token, and a [SEP] was added to the end. In succession, each token was indexed, the length of each sentence was compared to the maximum length, and padding was applied for those not reaching the maximum. Then, an attention mask was generated.
BERT is a two-way language model in which context is considered based on a transformer model. BERT differs from other models in that it conducts unsupervised pre-learning in both directions. The language model provides the probability distribution for the order of words. Through this two-way learning, performance is improved during the fine-tuning stage using the BERT pre-learning model.
A BERT model requires powerful graphical processor units (GPUs), and ours was implemented using Google Colab’s Tesla T4. Our study is based on the Hugging Face model based on Transformers for PyTorch and TensorFlow 2.0. The input value required an attention process to improve learning efficiency after fixing the length. In this study, the maximum length was set to 384 based on the average length and learning efficiency of the writing test answer. The pre-trained BERT model of Oh Yeon-taek used Google’s SentencePiece to learn 32,000 vocabulary words using about 180 million sentences from Wikipedia, and news data it achieved 87.8% from KorQuAD [11,12].
3.2 Model Implementation
3.2.1 Data collection and input
The data contained information collected from 2013 to 2017 from the Korean language education institution at K. University in Seoul, Korea. The dataset included 586 answers about “my hobby” and “memorable travel,” as obtained from Level-3 (Intermediate 1) writing answers for mid-term and final exams (Table 1). The general criteria of Test of Proficiency in Korean (TOPIK) Level 3 are described as follows [13]:
The individual has no problem doing normal, day-to-day activities and has the basic language skills to use various public facilities and maintain interpersonal relationships. He/she can understand and express him/herself regarding not only familiar and specific topics but also familiar social issues in paragraphs. He/she is also capable of distinguishing the basic features of colloquial language and literary language and can understand and use the two forms of language him/herself.
Distribution of answer sheets by score
3.2.2 Data analysis
From the data, the distribution of English special characters, answer length distributions, and score data were analyzed. In the case of “my hobby” 4.85% of sentences included question marks, 100% included periods, 8.63% included uppercase alpha characters, and 54.72% included numbers. In the case of “memorable travel,” the ratio of sentences that included question marks was 3.72%, the period was 100%, the sentence including capital letters was 10.23%, and the sentence including numbers was 80.47%.
In the case of uppercase characters, proper nouns, such as place names and anthroponyms, were written in English. Factors, such as special characters, numbers, and punctuation, slowed calculation speeds, interfering with learning. The distribution of answer lengths was as follows. Among the “my hobby” answers, the maximum length was 736 words, and the minimum was 57 words. The average was 506.19 words, the standard deviation was 114, and the median was 523. Among the “memorable travel” answers, the maximum length was 833 words, and the minimum was 137 words. The average was 618 words, the standard deviation was 112 words, and the median was 630 words. The length of the answer to “memorable travel” was longer than that of “my hobby.” Descriptive statistics are shown in Table 2.
The distribution of answers by score group is as follows. The average score of “my hobby” was 64.3, and that of “memorable travel” was 72.5. The median value was 70.
As shown in Tables 2 and 3, because the null hypothesis cannot be rejected: the correlation between the length of the answer and the score is verified. The results are shown in Table 4. As a result of checking the correlation between the score and the length of the answer, the topic, “my hobby,” has a correlation coefficient between the length of the answer and the score of 0.477. The correlation coefficient between the length of the answer and the score is 0.495.
Descriptive statistics of answers by length
Descriptive statistics of answers by score group
Correlation between the lengths of the answers
3.2.3 Data pre-training
The dataset contained 586 answers from midterm and final exams of foreign Korean-language learners at Level 3 (Intermediate 1) Korean proficiency. The pre-training was carried out as follows. First, the writing test data of third-grade learners were transcribed into a text file. Second, personal information and topics were deleted using the Notepad++ program as a pre-processing process. Third, all personal data, carriage returns, punctuation marks, and special characters were removed. Then, single topics were integrated into single lines to facilitate text processing. Commands and scores were tagged at the ends of the lines to enable automatic processing.
Data processing was conducted based on the Hugging Face model. The use of GPUs is essential to BERT model success. There are several GPU methods, such as utilizing cloud services (e.g., Google Korab) or implementing a local environment. In this paper, Google Colab’s Tesla T4 was used.
Data were prepared following transformer installation to load training and testing datasets. The data were divided into 70% versus 30% portions: one for training and the other for testing, respectively. During the verification stage, 10% of the training data were used. The next step was conversion according to the input format of BERT, and the Korean Sentence Splitter library was used to separate the converted content into sentence units. Then, the score labels were separated. The tokenizer step was next, which tokenized the content using BERT’s tokenizer. This process converts tokenized sentences into numeric indices, calculates the maximum sequence length of the input token, converts each token into a numeric index, matches the sentence to the maximum sequence length, and fills the deficiencies with padded zeroes.
The next step initializes the attention mask, which sets the attention mask to one without padding and zero with padding. Padding is not performed in the BERT attention layer so that the speed can be improved. After separating the data into training and verification sets, the attention mask is also separated into training and verification. Then, the data are converted into a PyTorch tensor. Then, the batch size is set, and it is entered using PyTorch’s DataLoader, where it is tied to the mask and labeled to set the data. During learning, data are imported based on the batch size, and to preprocess extracted review sentences, they are converted according to the input format of BERT; then labels are extracted.
3.2.4 Data learning
A bidirectional language model was created using BERT for data learning (Fig. 3). When dealing with text, this kind of model is advantageous because the problem of word ambiguity can be solved, even if the same word has different embedding vector values based on sentence form and location. The classification model leveraged 11 layers and an attention method. The optimizer, learning rate, and epoch value were then set. Next, data training was conducted. For reproduction, the random seed was fixed, and the gradient was initialized. During this process, a loss function was set for accuracy calculation.
Next, the accuracy was obtained by multiplying the value by 100; then, training was performed repeatedly according to the number of epochs. Data were imported repeatedly based on the batch setting size in the DataLoader. Then, the batch was sent to the GPU, where data were extracted, and the loss value was obtained. Finally, the average and total loss values were calculated.
Fine tuning model for automatic scoring.
4. Performance Analysis
4.1 Experimental Environment
The BERT program used in this paper for automatic scoring was implemented using Windows 10 with 16-GB memory, Python 3.6, TensorFlow 2.2, PyCharm, and a Google Colab GPU Tesla T4. The Adam-W optimizer was used with a learning rate of 0.00003 to set up the generative adversarial network (GAN) experiment. The contents related to the experimental environment are shown in Table 5.
The answers, including scores and labels, were used for automatic scoring. Among the descriptive answers written by foreign learners, a total of 586 answers were used, which included 371 corresponding to “my hobby” and 215 answers under the topic of “memorable travel.” The data were divided into 70% versus 30% portions: one for training and the other for testin0g, respectively. During the verification stage, 10% of the training data were used.
For the WScore model creation, the BERT model utilizing GPU for classification was created. The optimizer was set to AdamW, and the learning rate was set to 0.00003 with an epoch size of 40. The hyperparameters of the BERT-based WScore are shown in Table 6.
Hyperparameters of the BERT-based WScore
4.2 Result of the Experiment
For model learning, accuracy calculation and loss functions were set to reflect the order of scores. Next, a third accuracy was obtained by multiplying the value by 100; then, training was performed repeatedly based on settings. The data loader repeatedly moved data of the respective batch sizes into the GPU. The data were extracted from the batch to calculate the average loss value. Through this process, the accuracy was confirmed as follows. As shown in Table 7, the accuracy was 84.49% at one epoch, and when it reached 20 epochs, the last accuracy was 93.62%. It was the highest at 40 epochs. It was confirmed that, even if the number of epochs was increased to 50 and 100, the accuracy no longer increased.
5. Conclusion
This paper used a BERT transfer language model to apply an automatic scoring standard to Korean-learning writing tests for foreign learners to assess their efficacy. The data included score labels attributed to descriptive answers written by foreign learners, including their test scores. 371 answers were assessed under the topic, “My hobby,” and 215 were assessed under the topic, “Memorable travel.” For a total of 586 subjects, the training and testing datasets were divided 70 vs. 30%, and 10% of the training data were used for verification. For the automatic scoring of descriptive answers, the BERT-based WScore model was developed, achieving 95.80% accuracy, which is high compared with the methods assessed in previous studies. The most important part of the proposed descriptive scoring method depends on the scoring consistency, which is judged based on valid criteria. Hence, if these scoring criteria and processes can be automated, scoring costs and the temporal burdens of large-scale evaluations can be greatly reduced. This study provides the opportunity to further improve automatic scoring capabilities that analyze foreign learners’ written answers. In the future, the efficiency and consistency of writing test scoring will benefit by establishing large-scale learner datasets and setting standards for Korean language evaluation. However, pertaining to the scoring of the formative evaluation dimension of writing education, student feedback should be centered. Hence, modified approaches are needed.