Intelligent Question Answering System by Deep Convolutional Neural Network in Finance and Economics Teaching

Abstract

The question answering link in the traditional teaching method is analyzed to optimize the shortcomings and deficiencies of the existing question-and-answer (Q&A) machines and solve the problems of financial students’ difficulty in answering questions. Firstly, the difficulties and needs of students in answering questions are understood. Secondly, the traditional algorithm principle by the Q&A system is introduced and analyzed, and the problems and defects existing in the traditional Q&A system are summarized. On this basis, deep learning algorithms are introduced, the long short-term memory (LSTM) neural network and convolutional neural network (CNN) are combined, and a Q&A system by long short-term memory-convolutional neural network (LSTM-CNN) is proposed, the gated recurrent unit (GRU) attention mechanism is introduced, and the algorithm is optimized. Finally, the design experiments to determine the nearest parameters of the neural network algorithm and verify the effectiveness of the algorithm are carried out. The results show that for the LSTM-CNN, the effect is the best when dropout = 0.5. After introducing the attention mechanism optimization, the effect is the best when dropout = 0.6. The test results of the comparison between the recommended algorithm and the traditional Q&A model algorithm show that the LSTM-CNN algorithm maintains the ability of the LSTM algorithm to arrange information in chronological order. After being combined with the CNN algorithm, the language features of the sentence can be extracted more deeply, the semantic feature information can be captured more accurately from the sentence, and better performance can be maintained when processing more complex sentences. The introduction of a BANet can simultaneously obtain the past and future information so that the algorithm can more appropriately combine it with the context to retrieve the semantic features, and the effectiveness of the model has been greatly improved. The research results have played an optimizing role in improving the Q&A effect of finance and economics teaching and provided a reference for research in related fields.

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. Introduction

The continuous update of the Internet technology marks the arrival of the era of big data, and high-speed information communication means that a large amount of knowledge is being updated on the Internet all the time [1, 2]. People communicate and learn through the Internet. As an important tool for people to spread new things and acquire new knowledge, the Internet has been deeply integrated into people’s daily life and learning and is closely related to people’s lives. In today’s education field, online education is a very hot topic [3, 4]. Online education has significant advantages compared with education and training in the traditional sense.

In recent years, more complex applications have been constructed, and the application scope of natural language processing technology has been greatly expanded with the rapid development of deep learning. In education-related fields, the question-and-answer (Q & A) system has received extensive attention from all aspects. Major enterprises and well-known universities have invested a lot of energy and resources in the research and development of the Q & A system. However, the existing Q & A system and chat robots on the market still have certain shortcomings [5, 6]. The Q & A can generally be divided into two categories according to the field involved. One is a task-based Q & A, and the other is an open Q & A [7, 8]. The open Q & A covers all aspects and a wide range. The task-based Q & A system only answers questions about specific tasks, usually focusing on specific areas, such as travel, assistants, weather forecasts, and shopping mall customer service robots.

This paper applies deep learning technology to the field of financial education, to improve the accuracy and ease of use of the education system’s answering questions, thereby improving the teaching effect of finance and economics majors. Introducing the Q & A system in financial education, browsing related literature, financial education, and economics, and investigating and analyzing the common methods of the existing Q & A system. The paper discusses the shortcomings of the Internet domain systems in answering questions and traditional research techniques. The attention mechanism of various deep learning technologies by deep neural networks is introduced, and a Q & A system model by a deep convolutional neural network (CNN) is proposed through their organic combination. Through analysis, the model proposed is compared with the existing technical question and answer to verify the effectiveness of the method used.

There are four sections. The first section is the introduction. This part discusses the application of deep learning algorithms in the design of question answering systems and confirms the research ideas. The second section includes theory and method. This part summarizes the problems and shortcomings of the question-answering link in traditional teaching methods, summarizes the shortcomings of the current market question answering system, and proposes a question answering model based on LSTM-CNN hybrid neural network, explaining the details of optimization. The third section discusses the results of the research. This part analyzes the performance of the model, introduces related research to contrast with this research, and highlights the research results. The fourth section is a conclusion. This section includes actual contributions, limitations, and prospects.

2. Materials and Methods

2.1. Analysis of Traditional Teaching Methods by Question Answering

The traditional basic teaching method is in the school classroom, where the teacher teaches face-to-face, and the students listen to the lesson in a fixed place. In China, this kind of education has existed for thousands of years since ancient times and has many outstanding advantages. However, with the rapid development of time, some of its weaknesses have become more and more obvious. First, China attaches great importance to education, continuously carries out educational reforms, and vigorously promotes universal education, which has led to a continuous increase in the number of students. It is difficult for limited teachers to take care of every student Meanwhile, in addition, it is also different in students’ personalities, way of thinking, and ability to understand problems. The same teacher’s teaching methods are usually relatively fixed, which causes differences in the understanding and mastery of classroom content by different students [9, 10]. These differences continue to accumulate over time, and the problems of students continue to accumulate, and the pressure of learning will be greater, which will eventually lead to learning fatigue.

In response to these problems, the solution under the traditional teaching model is to conduct Q & A activities, that is, teachers usually answer and explain students’ questions at a specific time. It is limited by time and number of people. Answer questioning activities can only solve part of the problems, and most of the students’ questions are difficult to get a good answer [11]. Nowadays, with the rise of Internet technology and online education, the Internet can be used efficiently and conveniently to make up for the shortcomings of traditional education forms. Online education is a form of education that teaches through the Internet. It allows students to actively acquire and integrate knowledge in their free time and find out and conduct targeted learning by real-world learning conditions.

The most common way for students to acquire knowledge is to use search engines. However, traditional commercial search engines are not completely developed for students, and there are many drawbacks [12]. 1. The Internet search engines contain a lot of relevant information. When searching for a certain keyword, there is too much feedback to the user. This situation will cause users to enter many keywords or perform multiple searches when obtaining the information, they need to filter out the relevant information and find the content they need. Moreover, due to the subjective selection of customers, important related concepts are likely to be missed, resulting in incomplete search information and a series of errors. 2. Commercial search engines will inevitably contain a lot of advertising information, and search engines will return many relevant web pages, making it more difficult for users to extract information. 3. The search engines provided by the Internet can only match according to keywords or key sentences, but many keywords and key sentences need to have a certain logical combination to achieve the correct expression effect. Therefore, traditional search engines require users to construct keywords logic combinations to complete the query, which makes it difficult to use natural language to recognize the query entered by the user. This method is not user-friendly. For some complex or special search requirements, simple keywords are not enough to express the intent of the query. 4. The returned information field is too large [13]. For commercial search engines, the response returned for their commercial purposes may contain different fields, maybe mixed with commercial advertisements, and there is no clear target audience recommendation, so it may not meet the educational requirements.

2.2. Traditional Algorithm by Q & A System Model

The main purpose of the Q & A system model is to provide accurate answers to the questions asked by users in a certain field. Therefore, information retrieval has become the main method used in many task-based specific Q & A. Calculate the similarity of all candidate question texts in the Q & A library submitted by the user, and finally select the matching answer with the highest similarity to the question, and then return the text to the user. The following methods are currently more commonly used.

2.2.1. Term Frequency-Inverse Document Frequency Method

The term frequency-inverse document frequency (TF-IDF) is often used in information research and data mining to evaluate the importance of a word to a set of documents or one of the documents in a corpus. After some transformations, it can be used to calculate the similarity between texts [14]. Among them, the term frequency (TF) refers to term frequency, that is, the number of times a word appears in a document. The inverse document frequency (IDF) is the inverse document frequency.

The main idea of TF is to select a word with high frequency in text and compare it with other text information. If the word appears very few times in other texts, it means that the word can be used as a reference to distinguish this article from other documents [15, 16]. Different from the idea of the TF method, the IDF mainly screens documents containing specific words. The smaller the number of such documents, the higher the IDF value, which means that the word has good classification ability. The TF is expressed as equation (1), and the ITF is defined as equation (2).$$\begin{array}{}\text{(1)}& f_{i,j}=\frac{n_{i,j}}{{\displaystyle {\sum }_k{n}_{k,j}}},\text{(2)}& td{f}_i=\log \frac{D}{j:t_i\in d_j}.\end{array}$$

The numerator in equation (1) represents the number of occurrences of the word in the document. The denominator represents the sum of the number of occurrences of all words in the file. In equation (2), the numerator is the total number of documents in the corpus. The denominator is the number of documents containing a specific term. Since the words appearing are probably not in the corpus, the denominator is 0 and cannot be counted. Usually, add 1 to the denominator to prevent this from happening. Finally, TF-IDF is as$$\begin{array}{}\text{(3)}& tfid{f}_{i,j}=t{f}_{i,j}\times id{f}_i.\end{array}$$

Equation (3) can be defined as frequent words in a specific file and infrequently archived words in the entire document set, which can generate a high-weight TF-IDF value. Therefore, the TF-IDF tends to filter common words to retain important words. This word can be used as a keyword in a document. But if there are several identical keywords between two documents or sentences, then the two documents or sentences can be semantically similar.

Using TF-IDF to calculate semantic similarity, first this algorithm is used to search for keywords in phrases. Then, the keywords are merged in each phrase into a set and hit the word frequency. Finally, the semantic similarity of the two problems of calculating the cosine similarity of the two vectors transformed into the vector is obtained. This method of calculating semantic similarity by the TF-IDF algorithm has several disadvantages. (1) According to the nature of TF-IDF itself, if there are some feature words with high word frequency in the document but not relevant to the description result, these meaningless words are filtered out by reducing the TF-IDF value of the feature word. Meanwhile, its value TF-IDF will be lower. In practice, the IDF also has some disadvantages. Words frequently appearing in the document category usually characterize this type of text. This word has a higher weight. This type of feature words should be used to distinguish them from other types of text. (2). This method does not display the semantic information of the question sentence, so it is not suitable for more complex sentences and situations that require contextual information for logical operations.

2.2.2. Edit Distance Method

Edit distance refers to the minimum number of changes required to convert one string to another and is usually used to compare the similarity between two strings. It mainly shows the minimum number of editing tasks required to convert two strings into another string. The more the times needed are, the less similar they are.$$\begin{array}{}\text{(4)}& \text{Similarity}=1-\frac{d}{\min s1\text{length},s2\text{length}}.\end{array}$$

In equation (4), the numerator represents the minimum edit distance, and the denominator represents the larger of the two strings. The semantic similarity edit distance calculation method is essentially non-machine learning by dynamic programming. Due to the high computational complexity of this algorithm, when the Q & A corpus is large, the computational speed is very slow. Such as TF-IDF, it does not explain the contextual meaning of question sentences. It can only calculate the performance of the same words, so it has certain limitations.

In addition to the two traditional algorithms by Q & A models introduced, there are also Q & A model algorithms by knowledge graphs and Q & A algorithms by free text. However, most of the existing search algorithms are used to answer questions, and they have large defects in the processing of synonyms and contextual information. But in semantic processing, this is a very important point. Meanwhile, due to the lack of meaning to characterize query statements, the stability of the model is reduced, and more complex query statements cannot be correctly processed. Therefore, variants of LSTM and CNN for feature extraction and semantic recognition are used. Using the word vector in the input layer to model the meaning of the sentence, to realize the optimization of the traditional Q & A algorithm.

2.3. Deep Learning-Related Technologies

The basic structure of deep neural network (DNN) is shown in Figure 1. A general DNN consists of three components, the input layer, the hidden layer, and the output layer [17]. Usually, the first layer is the input layer, the last layer is the output layer, and all the layers in the middle are hidden. There is a fully connected relationship between the three layers, as shown in Figure 1.

[figure omitted; refer to PDF]

In the structure of a fully connected DNN, the lower layer neurons and all upper layer neurons can form connections. The potential problem is the expansion of the number of parameters, which is prone to overfitting and local optimal solutions [18, 19]. The CNN is a feedforward neural network with convolutional calculation and deep structure and is one of the representative algorithms of deep learning. For CNN, not all neurons in the upper and lower layers can be directly connected, but through the “convolution kernel” as an intermediary. The same convolution kernel is shared in all images, and the image still retains the original positional relationship after the convolution operation. To a certain extent, the CNN has the ability of characterization learning and can realize translation-invariant classification of input information according to its hierarchical structure. The CNN is an input-output mapping that can learn many mapping relationships between the input and output without the need for precise mathematical relationships between the input and output. If the network is formed according to a defined pattern, it can map the input and output of the network. A typical CNN includes an input layer, a convolutional layer, a pooling layer, a fully connected layer, and an output layer [20, 21]. The structure of the weight-sharing network is more like a biological neural network, which can reduce the number of weights in each layer, thereby reducing the complexity of the network model. The basic structure of CNN is shown in Figure 2.

[figure omitted; refer to PDF]

Most of the traditional retrieval algorithms used in Q & A have some problems. For example, they may ignore polysemous words, synonyms, and contextual information, which are all important in natural language processing [22, 23]. In addition, these algorithms seldom characterize the semantics of question sentences, which leads to the model’s low robustness and cannot cope well with more complicated question sentences. Therefore, the input layer is used word vectors to model the semantics in the sentence. The variant of LSTM-CNN is introduced for feature extraction and semantic recognition. The maximum pooling layer is used to merge and reduce the dimensions of the features extracted by each convolution kernel. Each component of the model will be introduced separately and a schematic diagram of the model will be given.

The K-nearest neighbor (KNN) is one of the simplest machine learning algorithms. When judging a sample, if most of the k-samples closest to the feature space sample belong to the same category, the sample belongs to that category. When used for text similarity calculation, it should be used in combination with other algorithms. The main steps of the algorithm are shown in Figure 3.

[figure omitted; refer to PDF]

The flow of the k-proximity algorithm is shown in Figure 3. First, the corpus is preprocessed and vectorized, and calculated according to the TF-IDF value of the feature word. After that, when the new text arrives, the vector of the new text is calculated according to the feature word. The K texts closest to the entry problem are selected in the training text, and the similarity by the cosine of the angle is measured between the vectors. For K similar texts in the new text, the weight of each category, in turn, is calculated. The weights of each category, training samples, and samples belonging to the K category have the same test similarity. The weights of the categories are compared and the text is placed with the highest weight in the class. The KNN algorithm is relatively simple and easy to use, but when the sample size is unbalanced, there will be large categories, such as when the sample size of one category is much larger than the sample size of other categories. To interpret large numbers with the K neighbors of the new input sample, a suitable K value must be found. And the KNN has a relatively large amount of calculation and cannot use contextual semantics.

In addition to the above algorithms, the research also involves the LSTM algorithm. The basic structure of the unit of the LSTM neural network is shown in Figure 4.

[figure omitted; refer to PDF]

In Figure 4, the LSTM is composed of a unit structure. The unit structure includes three important parts, the forget gate, the input gate, and the output gate. The function of the forget gate is to determine which data needs to be transmitted downwards and to forget the information that does not need to be transmitted downwards. The function of the input gate is to determine what content is added to the network and transmit it downward. The function of the output gate is to determine the information output to the next unit structure.

2.4. CNN-Based Q & A Model

The CNN-based hybrid neural network model is designed, the LSTM neural network is introduced, and the LSTM is introduced in detail. Combining it with the CNN model, introducing the pooling layer in detail and selecting the maximum pooling operation to merge and reduce the dimensionality of the output of the CNN layer. Finally, the structure diagram of the hybrid neural network model by CNN is given. By the CNN hybrid network model, the structure of the entire hybrid neural network model is shown in Figure 5.

[figure omitted; refer to PDF]

In Figure 5, most of the traditional retrieval algorithms have similar problems for the Q & A system. They ignore polysemous, synonymous words and contextual information. These are more important in natural language processing. In addition, these algorithms rarely describe the semantics of questions. This leads to the model’s low robustness, and it cannot cope well with more complex questions. Therefore, the word vector is used to model the semantics of the sentence and then enter the layer. The LSTM-CNN is introduced for feature extraction and semantic recognition, and the maximum pooling layer is used to merge and reduce the dimensions of the features extracted by each convolution kernel.

The above model is further optimized, the attention mechanism is introduced, and the LSTM is replaced by the gated recurrent unit (GRU). In the GRU-CNN model, some response sequences are considered not to have a relatively high correlation. Even in general sentences, the contribution of sentence meaning matching is very small and the interference is great. Theoretically speaking, reducing the accuracy of matching can change the order of response sentences to introduce an attention mechanism to further improve the original method of model performance. In both directions, rearrange the original input order of the model and set the two inputs to be bidirectional, so that the information before and after use can be obtained at the same time. The optimized neural network model architecture is shown in Figure 6.

[figure omitted; refer to PDF]

In Figure 6, the model is introduced into the attention mechanism. The GRU is used to replace the LSTM unit. Then, those are organically combined to construct a neural network model based on bidirectional GRU and attention mechanism. The bidirectional GRU can not only get the previous information but also get the information after the current location. An attention mechanism is introduced to increase the semantic matching weight of key feature words. These more important words can be more involved in the calculation of feature representation. The main parameter settings for this model are shown in Table 1.

Table 1

Model training parameters.

Parameter	Batch size	Learning rate	GRU layers	GRU layer size	Attention dimension	Number of iterations	Drop out
Parameter value	128	0.01	1	256	100	300	0.6

2.5. Experimental Design and Evaluation Index

For the Q & A system, the question and answer have a one-to-one correspondence, and it can also be regarded as an information retrieval or classification problem. This paper uses some retrieval models for experiments, and then compares and analyzes the effects with the models proposed.

The accuracy and mean reciprocal rank (MRR) are used to evaluate the final effect of the model. The detailed definition of accuracy is as follows:$$\begin{array}{}\text{(5)}& \text{accuracy}=\frac{\text{TP}}{\text{TF}+\text{TP}},\end{array}$$where $\text{TP}$ is the number of samples with correct answers, and $\text{TF}+\text{TP}$ is the total number of test samples. The definition $\text{MRR}$ is as follows:$$\begin{array}{}\text{(6)}& \text{MRR}=\frac{1}{Q}{\displaystyle \sum _{i=1}^Q\frac{1}{{\text{rank}}_i}}.\end{array}$$

In equation (6), $Q$ represents the sample set, and ${\text{rank}}_i$ represents the first correct answer in the $i$^th sample. The higher the order of the answer, the higher the accuracy and the better the quality of the returned sample set.

The data source of this research is the question bank of financial professional examinations in some colleges and universities. The crawler technology is used to select data for this database. It contains a total of 50,000 topics of various types. Among them, the proportion of multiple-choice questions accounted for more than 65%. In the end, after the screening, 10,000 test questions are obtained for experimental testing.

3. Results

3.1. Dropout Parameter Selection Experiment Results

The deep learning architecture is becoming more and more in-depth. The dropout is becoming more and more common to avoid the occurrence of overfitting. The dropout refers to the temporary removal of specific neurons from the network with a certain probability during the training process of the neural network. This can speed up training and prevent overfitting. The selection result of the neural network training parameters is shown in Figure 7.

[figures omitted; refer to PDF]

In the neural network dropout parameter selection result in Figure 7(a), 0.1 is used as the step size to test the neural network training effect from 0.2 to 1.1. For the LSTM-CNN neural network, the effect is best when the dropout = 0.5. When the dropout < 0.5, the overall training effect gradually rises, reaches its peak at 0.5, and then gradually begins to decline. The reason for this situation is that too many neurons are discarded, and it also leads to the loss of a lot of feature information, which reduces the training effect of the algorithm. After the introduction of attention mechanism optimization, the situation is similar, but the effect is best when the dropout = 0.6.

For the selection of the number of layers of the hybrid neural network model, it is usually controlled within 3 layers. Layer 1, layer 2, and layer 3 were selected for simulation training. Increasing the number of layers would slow down the fitting speed and would not significantly affect the experimental efficiency. In the end, the experiment used a single-layer neural network.

3.2. Comparison of Results of Traditional Retrieval Methods

This research conducted experiments by different retrieval methods, and the experimental results are shown in Figure 8.

[figures omitted; refer to PDF]

From the comparison results of the accuracy and MRR value of the traditional search algorithm shown in Figure 8(a), the Lucene search engine and the TF-IDF method are similar in principle, but not as effective as Doc2Vec. The latter only searches for keywords and converts a sentence word into a word vector. Then, it is combined with the next sentence ID. Finally, map the entire sentence to the sentence vector and save some semantic information together. Therefore, it is relatively efficient and works best among the three search engines. From the comparison result of the accuracy and the MRR value of the single neural network retrieval algorithm shown in Figure 8(b), for CNN, the semantic context information can be retained and the sentence can be expressed more accurately. Compared with other neural networks, the LSTM and GRU are superior to the CNN in processing text tasks because they can retain information over time. The GRU integration optimizes the gate structure, so the structure is simpler than LSTM, the parameters are smaller than LSTM, and the training speed is faster than LSTM. However, the impact of GRU on training is slightly worse than that of LSTM. Therefore, according to the actual situation, a neural network is selected for a specific application. The comparison results of the LSTM-CNN algorithm and optimized model shown in Figure 8(c) that the accuracy of LSTM-CNN and the proposed model is higher compared with the existing research model and the model that only uses the basic neural network model. The recommended database quality is also higher. Since the LSTM-CNN algorithm maintains the ability of the LSTM algorithm to arrange information in chronological order, after being combined with the CNN algorithm, the language features of the sentence can be extracted more deeply. The LSTM-CNN algorithm can capture semantic feature information from sentences and maintain better performance when processing more complex sentences. In the final proposed model, by the LSTM-CNN algorithm, a BANet is introduced. It can obtain the past and future information at the same time; the algorithm can more appropriately combine it with the context to retrieve the semantic features so that the effectiveness of the model has been greatly improved.

In summary, this paper proposed a neural network model that combines the LSTM-CNN hybrid neural network model and the BiGRU with the attention mechanism. Deep learning is applied to the field of junior high school teaching. This reduces the manual maintenance cost of the system, more accurately recognizes the user’s intent to ask questions, and better preserves the contextual semantics compared to the Q & A system based on traditional retrieval methods. Meanwhile, it improves the user experience and enables the question answering system to have higher accuracy and scalability based on deep learning. Finally, the effectiveness of the proposed model is proved to be in line with expectations through comparative experiments and experimental results analysis. Bi et al. [24] based on the BiLSTM model introduced the method of combining adaptive weight allocation technology and location context and proposed a smart method based on BiLSTM model with symptoms-frequency position attention (BLSTM-SFPA) Q & A system. This method enhances people’s attention to the typical symptoms of the disease [24]. Adjacent words are given more attention weight, and BLSTM-SFPA is proposed. The method of combining adaptive weight allocation technology and location context is introduced to enhance the focus on the typical symptoms of the disease and designed a data set comparison experiment in the medical field to verify the performance of the experiment based on the BiLSTM model. It not only combines the advantages of LSTM and CNN neural network to construct an improved LSTM-CNN hybrid neural network model, but also introduces a BiGRU neural network based on the GRU neural network, and organically combined with the attention mechanism. A BiGRU-attention neural network model is constructed. This model is applied to the teaching field of finance and economics courses. It makes up for the lack and deficiency of the Q & A system for finance and economics teaching.

4. Conclusions

Firstly, starting from the way of answering questions under the background of traditional education, the drawbacks of answering questions under the traditional teaching model are analyzed. Secondly, the search status of various chat and Q & A in related research fields is displayed, and some search and classification techniques used in existing Q & A are introduced. By the above analysis, combining the LSTM neural network and CNN in the deep learning algorithm, a neural network model for Q & A is established, and GRU is introduced for optimization. The attention mechanism and deep learning technology are applied to the field of finance and economics teaching, and the LSTM-CNN intelligent Q & A optimized by the attention mechanism is proposed to solve the problems and weaknesses of finance and economics students in their daily learning. The results show that, compared with the Q & A by traditional research methods, the Q & A model built using deep learning technology reduces the cost of manual system maintenance, more accurately identifies the user’s intention to ask questions, and can better understand the context for users. Providing a better application experience. Finally, the effectiveness of the proposed model is verified through the comparative analysis of the experimental and experimental results. Meanwhile, although the proposed algorithm model has achieved certain results, due to the limitation of research level and some objective factors, there are still some deficiencies in the research process. Firstly, the Q & A system proposed in this research uses a limited corpus. In future research, more high-quality corpora will be sought to make the research more convincing. Secondly, the hypothesis of this research is to use the form of the website or desktop client for human-computer interaction. If this idea wants to be put into practical application, it needs to be further optimized according to actual needs.

Acknowledgments

This work was supported by Guangzhou Huashang College, research on Huashang Cloud Accounting Industry College in 2020 (Project no. HS2019ZLGC13), and supported by Guangzhou Huashang College, research on the construction of management accounting information systems in 2018 (Project no. 2018HSXS01), and supported by Guangzhou Huashang College, research on first class professional construction of financial engineering based on university quality engineering in 2020 (Project no. HS2020ZLGC05).

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