# Evaluation of the Accuracy of Artificial Intelligence Translation Based on Deep Learning

Authors: Xiaojing Liu
Journal: Mobile Information Systems
 (2022)

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## Abstract

A machine automatic translation quality evaluation model based on a deep learning algorithm is built with the aim of achieving an accurate evaluation of machine automatic translation quality. Deep learning-based automatic machine translation was made. During the unsupervised and supervised learning stages, language information extraction executes unsupervised learning and reconstructs automated translation samples of bilingual words using a noise lessening automatic coding machine. To improve the effect of language vector feature extraction, the machine optimizes the effect of language vector feature extraction by importing language vector function and machine automatic translation information into bilingual words. The machine automatic translation language vector function is imported into the translation quality evaluation model based on deep learning to realize the automatic machine translation quality evaluation. The experimental results show that the constructed model can accurately evaluate the quality of machine automatic translation, the pattern of translated sentences and the number of sentences do not adversely affect the evaluation performance of the model, and the model exerts a good effect is showing.


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## Body

## 1. Introduction

Currently, translation product developers need to evaluate the machine automatic translation quality of translation products and analyze the effectiveness of translation products. Users need to know which translation products have excellent machine automatic translation quality in order to determine which translation products they need to use [1, 2]. Therefore, it is very important to evaluate the quality of automatic machine translation. Currently, all translated products are evaluated around specific fixed criteria for evaluating specific attributes of the product. It is difficult to assess the quality of automation because there are no fixed criteria for assessing the quality of automated machine translation. High-precision machine translation is used. Currently, many scholars are conducting some research on the quality evaluation of automatic machine translation. For example, Yiwei [3] evaluated the passive voice of Chinese using Google Translate and Youdao Translate as examples to solve the machine translation problem. In China’s Passive Voice Processing Machine Translation Quality Voice, Yiqun [4] in questionnaires and data analysis comprehensively evaluates translation software and compare specific differences in its indicators. The online dictionaries are used for automation as well [5] that gives not only the meaning of a word but reveals the related contextual information to word. Based on this, I researched the quality evaluation method of machine translation. Wenzhao and Defeng [6]. Google Machine Translation, for instance, assesses machine translation of English-related phrases into Chinese. Although the aforementioned researchers have conducted some research to assess the quality of machine autonomous translation, the current component remains unsolved. The method of evaluating the quality of machine translation has an issue of low accuracy. To successfully tackle this problem, the BP neural network algorithm which is a deep learning algorithm has been introduced.In the form of learning and training machine automatic translation of bilingual words, the BP neural network algorithm, a deep learning algorithm, can complete and achieve high accuracy in the language vector function of machine translation. Machine automatic translation is an assessment of the semantic quality of translation. To this end, this article uses the BP neural network algorithm to build a machine automatic translation quality evaluation model based on a deep learning algorithm. Deep learning algorithms are used in both feature extraction and quality evaluation steps to achieve high accuracy of postmachine language features. High-precision evaluation of automatic translation extraction and translation quality.


## 2. Machine Automatic Translation Quality Assessment Model

### 2.1. Automatic Translation Language Information Extraction

To optimize the effects of language vector feature extraction, we use two languages as an example and set the machine’s automatic translation quality as a hyperparameter by blending the two forms of language information using the machine’s automatic translation language information extraction approach based on deep learning. The learning/training stage in the language information extraction approach of automatic machine translation based on deep learning includes an unsupervised learning stage and a supervised learning stage. Bilingual words are concurrently learnt and trained by a noise reduction automated coding machine in the unsupervised learning phase to obtain the bilingual semantic properties of the two natural languages before and after translation. Standard information from the natural language corpus is fed into the bilingual word during the supervised learning phase, fine-tuning the bilingual semantic aspects of the two natural languages [7].


#### 2.1.1. Unsupervised Learning Stage

The training corpus and the natural language translation resultB that must be translated into natural language A are the learning targets of the unsupervised learning stage. Natural language translation result B is related to a different natural language than natural language A. The unsupervised learning step [8] is depicted in Figure 1 as a learning diagram. The training corpus and the natural language translation result B that must be translated into natural language A are the learning targets of the unsupervised learning stage. You can train the A vector YE and B vector YC of the machine automatic translation example y to obtain the bilingual alignment example pair YE¯, YC of the machine automatic translation example y. It represents a bilingual word. The noise reduction automatic encoder to implement unsupervised learning of bilingual words and reconstruct the A and B vectors of sample y was used. For the B vector in example y, to get the features of the language vector in the machine automatic translation example, we need to reconstruct the natural language A vector and the natural language B vector in this example.Figure 1
Schematic diagram of the unsupervised learning stage of learning.
(a)
Natural languageA vector reconstruction(b)
Natural languageB vector reconstructionTo optimize the reliability of unsupervised learning, a noise-reducing autoencoder is used to implement unsupervised learning of bilingual words. Before reconstructing, theA vector and B vector in the sample y, a certain degree is introduced to the sample pair YE,YC noise. The vector after introducing noise into the sample pair YE,YC is YE¯,YC¯. As an encoder for two languages, the noise reduction automatic encoder can obtain the implicit expression kE and kC of natural language A and natural language translation result B through sigmoid activation function encoding, which is
(1)kE=gθYE¯=rVE⋅YE¯+β,(2)kC=gθYC¯=rVC⋅YC¯+β.Among them,gθ and r are the coding function and the Sigmoid activation function, respectively; the translation matrix parameters transformed by A and B are VE and VC, respectively. VE and VC are bilingual words with their own language characteristics; because there is no difference in the dimensions of kE and kC, so the encoders of natural language A and natural language B share an offset value β.After obtainingkE and kC, a noise-reducing automatic encoder is used to decode the implicit expressions of the two languages in turn. Regarding the implicit expression kE of natural language A, the two decoders of natural language A and natural language translation result B are decoded in this article: kE is sequentially decoded into natural language A’s reconstruction vector yE⌢ and natural language translation result B reconstruction vector yC⌢:
(3)yE⌢=gθkE=rVE⋅kE+dE,(4)yC⌢=gθkC=rVC⋅kC+dC.Among them,gθ is the decoding function; dE and dC are the decoder offsets of the two natural languages. There is no significant difference between the decoding step of implicitly expressing kC and the decoding method of implicitly expressing kE. Decoding kC can obtain yC⌢, and decoding kE can obtain yE⌢.This type of encoding and decoding allows for language input; the vector is reconstructed into the vector of the original language and can also be reconstructed to vectors in other languages. But information between two natural languages is not the same and get a rebuild error during the rebuild. Enter settings in the pairYE,YC. For example, the reconstruction error is set to the article’s cross entropy mode. Set the sum of the following 5 types of reconstruction error as the loss in the unsupervised learning stage loss function:
(i)
The error between language vectors beforeYE reconstruction is pYE(ii)
The error ofYE reconstructing the original vector is pYC(iii)
The error ofYE reconstructing the language vector YC before translation is pYE,YC(iv)
The error ofYC reconstructing the language vector YE before translation is pYC,YE(v)
The error of reconstructing two natural language vector pairspYE,YC into the original (pretranslation) vector pair is pYC,YE,yC⌢,yE⌢. The loss function O is(5)O=pyE+pyC+pYE,YC+pYC,YE+pYC,YE,yC⌢,yE⌢The decoding function of unsupervised learning is set togθ=VE,VC,dE,dC, and the gradient descent algorithm is used to update the decoding function gθ until the loss function O approaches the least value where VE and VC are obtained by training.


#### 2.1.2. Supervised Learning Stage

During the supervised learning phase, you can reduce natural language reconstruction errors. The two natural language vector pairspYE,YC have all the information for the two languages y, so the natural language A and natural language B vector pairs pYE,YC are used in the conversion matrix and will be VE,VC. Real-time coding is needed to get bilingual information that implicitly represents kβ. An example of this exists in kβ.
(6)kβ=gθYE,YC=rVE,VC⋅YE,YC+β.Among them,β is the offset value of the encoder.The probabilities of positive and negative examples throughkβ operation are
(7)Q1=rϕkβ+β,(8)Q2=1−Q1.Among them,ϕ represents the weight; Q1 and Q2 are the probabilities of positive cases and negative cases in turn.Through supervised learning, the transformation translation matrixVE and VE embedded with translation information can be obtained, and they all have bilingual words belonging to language features [9], thereby optimizing the effect of natural language vector feature extraction.


### 2.2. Translation Quality Evaluation Model Based on Deep Learning

A translation quality assessment model based on deep learning consists of one visual layer, three hidden layers, and one regression layer. The obtained visual layer inputs are theVE and VE obtained in Section 2.1.1. The number of hidden layers is 100, and the three hidden layers are described in the order of t1, t2, and t3. The regression layer is the output layer and has one node. The joint probability distributions for the visible and invisible layers are
(9)Qθ=Qt1⋅Qt2⋅Qt3.Among them,Qt1, Qt2, and Qt3 are, respectively, the probability that the language variables of the three hidden layers meet the translation requirements.The procedure for evaluating a translation quality evaluation model based on deep learning is as follows:(i)
In deep learning networks, unsupervised training is implemented from top to bottom, each layer is set up as a restricted Boltzmann machine, the weights of each layer are trained using a greedy learning method, and training is from bottom to top. The first layer and the other layers are modeled as Gauss binary restricted Boltzmann machines and binary restricted Boltzmann machines. In a restricted Boltzmann machine, there is no connectivity between each visible node and a hidden node, and its conditional probability distributionθ1 and joint probability distribution θ2 are(10)θ1=Mfj+ε∑jϕt1,(11)θ2=logisticfj+∑jϕujεIn the formula,M and logistic are Gaussian density function and logistic function, respectively; the bias of the visible layer uj is uj; t1 represents the number of hidden layer nodes is 1, j=1,2,3; and ε represents the standard deviation.
(ii)
Supervised fine-tuning: the output layer implements overall fine-tuning according to the input supervised bilingual wordsVE and Vc with their own language characteristics(iii)
The weights and biases of each layer can be generated by unsupervised training and supervised learning. The regression model of output translation features can be built where the model can be used to assess machine automatic translation instructions. The model is(12)Ω=ϕ⋅Ve+ϕ⋅VcAmong them,Ω is the result of machine automatic translation quality evaluation.


## 2.1. Automatic Translation Language Information Extraction

To optimize the effects of language vector feature extraction, we use two languages as an example and set the machine’s automatic translation quality as a hyperparameter by blending the two forms of language information using the machine’s automatic translation language information extraction approach based on deep learning. The learning/training stage in the language information extraction approach of automatic machine translation based on deep learning includes an unsupervised learning stage and a supervised learning stage. Bilingual words are concurrently learnt and trained by a noise reduction automated coding machine in the unsupervised learning phase to obtain the bilingual semantic properties of the two natural languages before and after translation. Standard information from the natural language corpus is fed into the bilingual word during the supervised learning phase, fine-tuning the bilingual semantic aspects of the two natural languages [7].


### 2.1.1. Unsupervised Learning Stage

The training corpus and the natural language translation resultB that must be translated into natural language A are the learning targets of the unsupervised learning stage. Natural language translation result B is related to a different natural language than natural language A. The unsupervised learning step [8] is depicted in Figure 1 as a learning diagram. The training corpus and the natural language translation result B that must be translated into natural language A are the learning targets of the unsupervised learning stage. You can train the A vector YE and B vector YC of the machine automatic translation example y to obtain the bilingual alignment example pair YE¯, YC of the machine automatic translation example y. It represents a bilingual word. The noise reduction automatic encoder to implement unsupervised learning of bilingual words and reconstruct the A and B vectors of sample y was used. For the B vector in example y, to get the features of the language vector in the machine automatic translation example, we need to reconstruct the natural language A vector and the natural language B vector in this example.Figure 1
Schematic diagram of the unsupervised learning stage of learning.
(a)
Natural languageA vector reconstruction(b)
Natural languageB vector reconstructionTo optimize the reliability of unsupervised learning, a noise-reducing autoencoder is used to implement unsupervised learning of bilingual words. Before reconstructing, theA vector and B vector in the sample y, a certain degree is introduced to the sample pair YE,YC noise. The vector after introducing noise into the sample pair YE,YC is YE¯,YC¯. As an encoder for two languages, the noise reduction automatic encoder can obtain the implicit expression kE and kC of natural language A and natural language translation result B through sigmoid activation function encoding, which is
(1)kE=gθYE¯=rVE⋅YE¯+β,(2)kC=gθYC¯=rVC⋅YC¯+β.Among them,gθ and r are the coding function and the Sigmoid activation function, respectively; the translation matrix parameters transformed by A and B are VE and VC, respectively. VE and VC are bilingual words with their own language characteristics; because there is no difference in the dimensions of kE and kC, so the encoders of natural language A and natural language B share an offset value β.After obtainingkE and kC, a noise-reducing automatic encoder is used to decode the implicit expressions of the two languages in turn. Regarding the implicit expression kE of natural language A, the two decoders of natural language A and natural language translation result B are decoded in this article: kE is sequentially decoded into natural language A’s reconstruction vector yE⌢ and natural language translation result B reconstruction vector yC⌢:
(3)yE⌢=gθkE=rVE⋅kE+dE,(4)yC⌢=gθkC=rVC⋅kC+dC.Among them,gθ is the decoding function; dE and dC are the decoder offsets of the two natural languages. There is no significant difference between the decoding step of implicitly expressing kC and the decoding method of implicitly expressing kE. Decoding kC can obtain yC⌢, and decoding kE can obtain yE⌢.This type of encoding and decoding allows for language input; the vector is reconstructed into the vector of the original language and can also be reconstructed to vectors in other languages. But information between two natural languages is not the same and get a rebuild error during the rebuild. Enter settings in the pairYE,YC. For example, the reconstruction error is set to the article’s cross entropy mode. Set the sum of the following 5 types of reconstruction error as the loss in the unsupervised learning stage loss function:
(i)
The error between language vectors beforeYE reconstruction is pYE(ii)
The error ofYE reconstructing the original vector is pYC(iii)
The error ofYE reconstructing the language vector YC before translation is pYE,YC(iv)
The error ofYC reconstructing the language vector YE before translation is pYC,YE(v)
The error of reconstructing two natural language vector pairspYE,YC into the original (pretranslation) vector pair is pYC,YE,yC⌢,yE⌢. The loss function O is(5)O=pyE+pyC+pYE,YC+pYC,YE+pYC,YE,yC⌢,yE⌢The decoding function of unsupervised learning is set togθ=VE,VC,dE,dC, and the gradient descent algorithm is used to update the decoding function gθ until the loss function O approaches the least value where VE and VC are obtained by training.


### 2.1.2. Supervised Learning Stage

During the supervised learning phase, you can reduce natural language reconstruction errors. The two natural language vector pairspYE,YC have all the information for the two languages y, so the natural language A and natural language B vector pairs pYE,YC are used in the conversion matrix and will be VE,VC. Real-time coding is needed to get bilingual information that implicitly represents kβ. An example of this exists in kβ.
(6)kβ=gθYE,YC=rVE,VC⋅YE,YC+β.Among them,β is the offset value of the encoder.The probabilities of positive and negative examples throughkβ operation are
(7)Q1=rϕkβ+β,(8)Q2=1−Q1.Among them,ϕ represents the weight; Q1 and Q2 are the probabilities of positive cases and negative cases in turn.Through supervised learning, the transformation translation matrixVE and VE embedded with translation information can be obtained, and they all have bilingual words belonging to language features [9], thereby optimizing the effect of natural language vector feature extraction.


## 2.1.1. Unsupervised Learning Stage

The training corpus and the natural language translation resultB that must be translated into natural language A are the learning targets of the unsupervised learning stage. Natural language translation result B is related to a different natural language than natural language A. The unsupervised learning step [8] is depicted in Figure 1 as a learning diagram. The training corpus and the natural language translation result B that must be translated into natural language A are the learning targets of the unsupervised learning stage. You can train the A vector YE and B vector YC of the machine automatic translation example y to obtain the bilingual alignment example pair YE¯, YC of the machine automatic translation example y. It represents a bilingual word. The noise reduction automatic encoder to implement unsupervised learning of bilingual words and reconstruct the A and B vectors of sample y was used. For the B vector in example y, to get the features of the language vector in the machine automatic translation example, we need to reconstruct the natural language A vector and the natural language B vector in this example.Figure 1
Schematic diagram of the unsupervised learning stage of learning.
(a)
Natural languageA vector reconstruction(b)
Natural languageB vector reconstructionTo optimize the reliability of unsupervised learning, a noise-reducing autoencoder is used to implement unsupervised learning of bilingual words. Before reconstructing, theA vector and B vector in the sample y, a certain degree is introduced to the sample pair YE,YC noise. The vector after introducing noise into the sample pair YE,YC is YE¯,YC¯. As an encoder for two languages, the noise reduction automatic encoder can obtain the implicit expression kE and kC of natural language A and natural language translation result B through sigmoid activation function encoding, which is
(1)kE=gθYE¯=rVE⋅YE¯+β,(2)kC=gθYC¯=rVC⋅YC¯+β.Among them,gθ and r are the coding function and the Sigmoid activation function, respectively; the translation matrix parameters transformed by A and B are VE and VC, respectively. VE and VC are bilingual words with their own language characteristics; because there is no difference in the dimensions of kE and kC, so the encoders of natural language A and natural language B share an offset value β.After obtainingkE and kC, a noise-reducing automatic encoder is used to decode the implicit expressions of the two languages in turn. Regarding the implicit expression kE of natural language A, the two decoders of natural language A and natural language translation result B are decoded in this article: kE is sequentially decoded into natural language A’s reconstruction vector yE⌢ and natural language translation result B reconstruction vector yC⌢:
(3)yE⌢=gθkE=rVE⋅kE+dE,(4)yC⌢=gθkC=rVC⋅kC+dC.Among them,gθ is the decoding function; dE and dC are the decoder offsets of the two natural languages. There is no significant difference between the decoding step of implicitly expressing kC and the decoding method of implicitly expressing kE. Decoding kC can obtain yC⌢, and decoding kE can obtain yE⌢.This type of encoding and decoding allows for language input; the vector is reconstructed into the vector of the original language and can also be reconstructed to vectors in other languages. But information between two natural languages is not the same and get a rebuild error during the rebuild. Enter settings in the pairYE,YC. For example, the reconstruction error is set to the article’s cross entropy mode. Set the sum of the following 5 types of reconstruction error as the loss in the unsupervised learning stage loss function:
(i)
The error between language vectors beforeYE reconstruction is pYE(ii)
The error ofYE reconstructing the original vector is pYC(iii)
The error ofYE reconstructing the language vector YC before translation is pYE,YC(iv)
The error ofYC reconstructing the language vector YE before translation is pYC,YE(v)
The error of reconstructing two natural language vector pairspYE,YC into the original (pretranslation) vector pair is pYC,YE,yC⌢,yE⌢. The loss function O is(5)O=pyE+pyC+pYE,YC+pYC,YE+pYC,YE,yC⌢,yE⌢The decoding function of unsupervised learning is set togθ=VE,VC,dE,dC, and the gradient descent algorithm is used to update the decoding function gθ until the loss function O approaches the least value where VE and VC are obtained by training.


## 2.1.2. Supervised Learning Stage

During the supervised learning phase, you can reduce natural language reconstruction errors. The two natural language vector pairspYE,YC have all the information for the two languages y, so the natural language A and natural language B vector pairs pYE,YC are used in the conversion matrix and will be VE,VC. Real-time coding is needed to get bilingual information that implicitly represents kβ. An example of this exists in kβ.
(6)kβ=gθYE,YC=rVE,VC⋅YE,YC+β.Among them,β is the offset value of the encoder.The probabilities of positive and negative examples throughkβ operation are
(7)Q1=rϕkβ+β,(8)Q2=1−Q1.Among them,ϕ represents the weight; Q1 and Q2 are the probabilities of positive cases and negative cases in turn.Through supervised learning, the transformation translation matrixVE and VE embedded with translation information can be obtained, and they all have bilingual words belonging to language features [9], thereby optimizing the effect of natural language vector feature extraction.


## 2.2. Translation Quality Evaluation Model Based on Deep Learning

A translation quality assessment model based on deep learning consists of one visual layer, three hidden layers, and one regression layer. The obtained visual layer inputs are theVE and VE obtained in Section 2.1.1. The number of hidden layers is 100, and the three hidden layers are described in the order of t1, t2, and t3. The regression layer is the output layer and has one node. The joint probability distributions for the visible and invisible layers are
(9)Qθ=Qt1⋅Qt2⋅Qt3.Among them,Qt1, Qt2, and Qt3 are, respectively, the probability that the language variables of the three hidden layers meet the translation requirements.The procedure for evaluating a translation quality evaluation model based on deep learning is as follows:(i)
In deep learning networks, unsupervised training is implemented from top to bottom, each layer is set up as a restricted Boltzmann machine, the weights of each layer are trained using a greedy learning method, and training is from bottom to top. The first layer and the other layers are modeled as Gauss binary restricted Boltzmann machines and binary restricted Boltzmann machines. In a restricted Boltzmann machine, there is no connectivity between each visible node and a hidden node, and its conditional probability distributionθ1 and joint probability distribution θ2 are(10)θ1=Mfj+ε∑jϕt1,(11)θ2=logisticfj+∑jϕujεIn the formula,M and logistic are Gaussian density function and logistic function, respectively; the bias of the visible layer uj is uj; t1 represents the number of hidden layer nodes is 1, j=1,2,3; and ε represents the standard deviation.
(ii)
Supervised fine-tuning: the output layer implements overall fine-tuning according to the input supervised bilingual wordsVE and Vc with their own language characteristics(iii)
The weights and biases of each layer can be generated by unsupervised training and supervised learning. The regression model of output translation features can be built where the model can be used to assess machine automatic translation instructions. The model is(12)Ω=ϕ⋅Ve+ϕ⋅VcAmong them,Ω is the result of machine automatic translation quality evaluation.


## 3. Simulation Experiment

### 3.1. Model Validity Analysis

The dataset of this model is the text of the news site and uses “Translation Service Specification Part 1: Written Translation” (GB/T 19363.1-2003) created by the Translators Association of China as an evaluation index standard. The proposed model to assess the translation quality of trained sentences on news websites was used. The details of the news text are shown in Figure2.Figure 2
Details of the news statement.After the news text is automatically translated by the machine, as shown in Figure2, the quality of the translation using the proposed model was evaluated. There are 5,000 translated sentences in both the language training set and the test set. All sentence types are dependent compound statements. Detailed translation of different translation types is shown in Figure 3.Figure 3
Translation details.The test index is set to the difference between the evaluation result and the real scenario while testing the validity of a suggested model to assess the quality of automated machine translation. The evaluation results of the proposed model are depicted in Figure4. The comparison between Figures 3 and 4 shows the number of correct sentences automatically translated by the machine and the real number differ by one in the suggested model evaluation. Because each language has its own grammatical structure, it can be therefore argued that the model evaluation result may effectively calculate and assess the quality of the machine’s automatic translation and fulfil the needs of practical use.Figure 4
Proposed model evaluation results.


### 3.2. The Impact of Translated Sentence Patterns on the Evaluation Performance of the Proposed Model

Declaration sentences, special purpose sentences, question sentences, and complex sentences are the types of sentences used in automatic machine translation to and test and assess the translation quality of automatic machine translation. Figures5 and 6 show the evaluation outcomes. Figures 5 and 6 show that for different languages and translation patterns, the discrepancy between the proposed model’s evaluation results and the real scenario is low. Only the evaluation result of the specific sentence’s translation quality differs, although the variation is minor (1 piece). The assessment effect of the suggested model is unaffected by declaration sentences, question sentences, or compound sentences, and the evaluation results are consistent with the actual situation.Figure 5
Translation quality of declarative sentences and special usage sentences.Figure 6
Translation quality of interrogative sentences and compound sentences.


### 3.3. The Impact of the Number of Sentences on the Evaluation Performance of the Proposed Model

In the Bilingual Assessment Survey (BLEU) score, there is actual translation quality informationA1 and proposed model assessment findings A2 for each sentence. For the determination of percentage of A2, it is to find that how much of the proposed model evaluation result A2 will be seen in A1. The BLEU score is based on this ratio. The BLEU score can be used to calculate the evaluation performance of the proposed model by adding the translation errors of one sentence in the test set. Figure 7 depicts these outcomes. Figure 7 illustrates that the quantity of phrases and sentences does not have a major impact on the suggested model’s evaluation performance. The proposed model’s BLEU score rises from 96 to 98 as the number of phrases increases from 1000 to 6000. The results demonstrate that the proposed model is effective.Figure 7
The impact of the number of sentences on the evaluation performance of the essay model.To further analyze the validity of the proposed model, it is compared with the methods in Ref. [3] and Ref. [4] to obtain an accuracy comparison of the three models, as shown in Figure 8.Figure 8
Comparison of the accuracy of the three methods under different numbers of sentences.From Figure8, we can see that the accuracy of the machine automatic translation quality evaluation of the proposed model is much higher than the other two methods, even if the number of sentences is 1000, 2000, 3000, 4000, 5000, and 6000. It is stable with an accuracy of up to 97% or more and has a certain utility value. To better analyze the applied value of the proposed model, the quality evaluation efficiencies of the three models are compared again and the results are shown in Figure 9.Figure 9
The quality evaluation efficiency of the three methods under different numbers of sentences.From Figure9, it can be seen that even if the number of sentences is 1000, 2000, 3000, 4000, 5000, and 6000, the machine automatic translation quality evaluation efficiency of the proposed model is much higher while ensuring the accuracy of quality evaluation of the other two methods. The highest evaluation efficiency is over 95%, which can prove to some extent the feasibility of the proposed model.


## 3.1. Model Validity Analysis

The dataset of this model is the text of the news site and uses “Translation Service Specification Part 1: Written Translation” (GB/T 19363.1-2003) created by the Translators Association of China as an evaluation index standard. The proposed model to assess the translation quality of trained sentences on news websites was used. The details of the news text are shown in Figure2.Figure 2
Details of the news statement.After the news text is automatically translated by the machine, as shown in Figure2, the quality of the translation using the proposed model was evaluated. There are 5,000 translated sentences in both the language training set and the test set. All sentence types are dependent compound statements. Detailed translation of different translation types is shown in Figure 3.Figure 3
Translation details.The test index is set to the difference between the evaluation result and the real scenario while testing the validity of a suggested model to assess the quality of automated machine translation. The evaluation results of the proposed model are depicted in Figure4. The comparison between Figures 3 and 4 shows the number of correct sentences automatically translated by the machine and the real number differ by one in the suggested model evaluation. Because each language has its own grammatical structure, it can be therefore argued that the model evaluation result may effectively calculate and assess the quality of the machine’s automatic translation and fulfil the needs of practical use.Figure 4
Proposed model evaluation results.


## 3.2. The Impact of Translated Sentence Patterns on the Evaluation Performance of the Proposed Model

Declaration sentences, special purpose sentences, question sentences, and complex sentences are the types of sentences used in automatic machine translation to and test and assess the translation quality of automatic machine translation. Figures5 and 6 show the evaluation outcomes. Figures 5 and 6 show that for different languages and translation patterns, the discrepancy between the proposed model’s evaluation results and the real scenario is low. Only the evaluation result of the specific sentence’s translation quality differs, although the variation is minor (1 piece). The assessment effect of the suggested model is unaffected by declaration sentences, question sentences, or compound sentences, and the evaluation results are consistent with the actual situation.Figure 5
Translation quality of declarative sentences and special usage sentences.Figure 6
Translation quality of interrogative sentences and compound sentences.


## 3.3. The Impact of the Number of Sentences on the Evaluation Performance of the Proposed Model

In the Bilingual Assessment Survey (BLEU) score, there is actual translation quality informationA1 and proposed model assessment findings A2 for each sentence. For the determination of percentage of A2, it is to find that how much of the proposed model evaluation result A2 will be seen in A1. The BLEU score is based on this ratio. The BLEU score can be used to calculate the evaluation performance of the proposed model by adding the translation errors of one sentence in the test set. Figure 7 depicts these outcomes. Figure 7 illustrates that the quantity of phrases and sentences does not have a major impact on the suggested model’s evaluation performance. The proposed model’s BLEU score rises from 96 to 98 as the number of phrases increases from 1000 to 6000. The results demonstrate that the proposed model is effective.Figure 7
The impact of the number of sentences on the evaluation performance of the essay model.To further analyze the validity of the proposed model, it is compared with the methods in Ref. [3] and Ref. [4] to obtain an accuracy comparison of the three models, as shown in Figure 8.Figure 8
Comparison of the accuracy of the three methods under different numbers of sentences.From Figure8, we can see that the accuracy of the machine automatic translation quality evaluation of the proposed model is much higher than the other two methods, even if the number of sentences is 1000, 2000, 3000, 4000, 5000, and 6000. It is stable with an accuracy of up to 97% or more and has a certain utility value. To better analyze the applied value of the proposed model, the quality evaluation efficiencies of the three models are compared again and the results are shown in Figure 9.Figure 9
The quality evaluation efficiency of the three methods under different numbers of sentences.From Figure9, it can be seen that even if the number of sentences is 1000, 2000, 3000, 4000, 5000, and 6000, the machine automatic translation quality evaluation efficiency of the proposed model is much higher while ensuring the accuracy of quality evaluation of the other two methods. The highest evaluation efficiency is over 95%, which can prove to some extent the feasibility of the proposed model.


## 4. Conclusion

As a result of this study, a machine automatic translation quality evaluation model has been built. The model is based on deep learning algorithm and is tested through experiments. It is significant to note that the accuracy and precision of the evaluation result is high because the discrepancy between the numbers of correct sentences is automatically translated by the machine whereas the real number is 1 in the suggested model. The experimental results further says that the proposed model’s assessment result will differ somewhat from the actual situation if the language and pattern of the translated sentence is altered. The evaluation result of the special sentence’s translation quality will differ only.The suggested model’s BLEU score grows gradually as the number of sentences increases, reaching a maximum of 98 which accurately evaluate and realize the automatic machine translation quality evaluation. Based on evaluation results, it can be concluded that the proposed model is appropriate for assessing the quality of machine translation and possess huge amounts of potential when it comes to language learning and translation.



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*Source: 9513433-2022-05-20.xml*