# Design of English Automatic Translation System Based on Machine Intelligent Translation and Secure Internet of Things

Authors: Haidong Ban; Jing Ning
Journal: Mobile Information Systems
 (2021)

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## Abstract

With the rapid development of Internet technology and the development of economic globalization, international exchanges in various fields have become increasingly active, and the need for communication between languages has become increasingly clear. As an effective tool, automatic translation can perform equivalent translation between different languages while preserving the original semantics. This is very important in practice. This paper focuses on the Chinese-English machine translation model based on deep neural networks. In this paper, we use the end-to-end encoder and decoder framework to create a neural machine translation model, the machine automatically learns its function, and the data is converted into word vectors in a distributed method and can be directly through the neural network perform the mapping between the source language and the target language. Research experiments show that, by adding part of the voice information to verify the effectiveness of the model performance improvement, the performance of the translation model can be improved. With the superimposition of the number of network layers from two to four, the improvement ratios of each model are 5.90%, 6.1%, 6.0%, and 7.0%, respectively. Among them, the model with an independent recurrent neural network as the network structure has the largest improvement rate and a higher improvement rate, so the system has high availability.


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## Body

## 1. Introduction

### 1.1. Background

With the globalization of the world economy and the development of Internet technology, international communication has become active, the connections between people of different languages in each country have become increasingly deepened, and the demand for bilingual communication in social work and life is also increasing. Translation is especially important in this process. It is an important way to achieve equivalent communication between different languages. Traditional manual translation consumes a lot of manpower and financial resources and cannot achieve high efficiency. It is very important to obtain fast and effective computer translation results, and it is particularly important to replace human translation in the field of professional translation. At this stage, the incomplete translation result also means that there is still a lot of room for research in this field.


### 1.2. Significance

Artificial intelligence technology is a scientific research that allows machines to think like humans and realize human behavior. For human beings, language communication is indispensable, and linguistic issues are inseparable from human society. In terms of theoretical value, machine translation research has a comparative effect on natural language processing. Machine translation research may lead to the development of other fields. The advancement of neural machine translation technology has greatly promoted other fields of natural language, such as sentiment analysis, text classification, dialogue creation, and other editing tasks, which are also booming. More importantly, machine translation has a very high practicality. The rapid economic and social development of the world today, the construction of the “Belt and Road,” and the realization of a community with a shared future for mankind require language exchanges. It not only saves a lot of human and material resources required for translation but also makes the communication in economic, cultural, political, and other fields between people who use different languages in the world more convenient and effective.


### 1.3. Related Work

Castilho discussed a new paradigm in the field of machine translation-Neural Machine Translation (NMT) and compared the quality of the NMT system with statistical MT by describing three studies using automatic and manual evaluation methods. The automatic evaluation results proposed for NMT are very promising, but manual evaluation shows mixed results. Castilho reports that fluency has improved, but the results are inconsistent in terms of adequacy and postediting. NMT undoubtedly represents progress in the MT field, but the community should be careful not to oversell [1]. However, his experimental process is not closed, leading to discrepancies in experimental results. Choi first observed the potential weakness of the continuous vector representation of symbols in neural machine translation. That is, the continuous vector representation of the symbol or the word embedding vector encodes multiple dimensions of similarity, which is equivalent to encoding more than one meaning of the word. As a result, the encoder and decoder recurrent networks in neural machine translation need to spend a lot of capacity to disambiguate the source and target words based on the context defined by the source sentence. Based on this observation, Choi suggests using the nonlinear bag-of-words representation of the source sentence to contextualize the word embedding vector. He suggested using typed symbols to represent special signs (such as numbers, proper nouns, and abbreviations) to help translate words that are not suitable for translation through continuous vectors [2]. However, due to the uncertainty of the experimental process, there are still gaps in the experimental results. Wu research proves that grammatical knowledge is effective for improving the performance of neural machine translation (NMT). Most of the previous work has focused on using the source or target grammar in the encoder-decoder model based on recurrent neural network (RNN). In this paper, he uses both the source dependency tree and the target dependency tree to improve the NMT model. Wu proposed a simple but effective syntax-aware encoder to incorporate the source dependency tree into NMT. The new encoder enriches each source state with dependencies in the tree. Then, Wu proposed a novel sequence dependence framework. In this framework, the target transformation and its corresponding dependency tree are jointly constructed and modeled. During decoding, the tree structure is used as a context to facilitate word generation [3]. However, there are many influencing factors in this research process, so there are certain differences in experimental results.


### 1.4. Innovation

The innovation of this paper is as follows: (1) Aiming at the transformer machine translation model using multihead attention mechanism and feed-forward neural network, inspired by linguistic cognition, it is proposed that, on the basis of this model, it can be integrated into the coding process of the source language by adding part-of-speech information vector representation to improve the translation effect of the transformer model. (2) Aiming at the machine translation model with a cyclic neural network as the network structure, this paper introduces another cyclic neural network variant-independent cyclic neural network as the network structure of encoder and decoder.


## 1.1. Background

With the globalization of the world economy and the development of Internet technology, international communication has become active, the connections between people of different languages in each country have become increasingly deepened, and the demand for bilingual communication in social work and life is also increasing. Translation is especially important in this process. It is an important way to achieve equivalent communication between different languages. Traditional manual translation consumes a lot of manpower and financial resources and cannot achieve high efficiency. It is very important to obtain fast and effective computer translation results, and it is particularly important to replace human translation in the field of professional translation. At this stage, the incomplete translation result also means that there is still a lot of room for research in this field.


## 1.2. Significance

Artificial intelligence technology is a scientific research that allows machines to think like humans and realize human behavior. For human beings, language communication is indispensable, and linguistic issues are inseparable from human society. In terms of theoretical value, machine translation research has a comparative effect on natural language processing. Machine translation research may lead to the development of other fields. The advancement of neural machine translation technology has greatly promoted other fields of natural language, such as sentiment analysis, text classification, dialogue creation, and other editing tasks, which are also booming. More importantly, machine translation has a very high practicality. The rapid economic and social development of the world today, the construction of the “Belt and Road,” and the realization of a community with a shared future for mankind require language exchanges. It not only saves a lot of human and material resources required for translation but also makes the communication in economic, cultural, political, and other fields between people who use different languages in the world more convenient and effective.


## 1.3. Related Work

Castilho discussed a new paradigm in the field of machine translation-Neural Machine Translation (NMT) and compared the quality of the NMT system with statistical MT by describing three studies using automatic and manual evaluation methods. The automatic evaluation results proposed for NMT are very promising, but manual evaluation shows mixed results. Castilho reports that fluency has improved, but the results are inconsistent in terms of adequacy and postediting. NMT undoubtedly represents progress in the MT field, but the community should be careful not to oversell [1]. However, his experimental process is not closed, leading to discrepancies in experimental results. Choi first observed the potential weakness of the continuous vector representation of symbols in neural machine translation. That is, the continuous vector representation of the symbol or the word embedding vector encodes multiple dimensions of similarity, which is equivalent to encoding more than one meaning of the word. As a result, the encoder and decoder recurrent networks in neural machine translation need to spend a lot of capacity to disambiguate the source and target words based on the context defined by the source sentence. Based on this observation, Choi suggests using the nonlinear bag-of-words representation of the source sentence to contextualize the word embedding vector. He suggested using typed symbols to represent special signs (such as numbers, proper nouns, and abbreviations) to help translate words that are not suitable for translation through continuous vectors [2]. However, due to the uncertainty of the experimental process, there are still gaps in the experimental results. Wu research proves that grammatical knowledge is effective for improving the performance of neural machine translation (NMT). Most of the previous work has focused on using the source or target grammar in the encoder-decoder model based on recurrent neural network (RNN). In this paper, he uses both the source dependency tree and the target dependency tree to improve the NMT model. Wu proposed a simple but effective syntax-aware encoder to incorporate the source dependency tree into NMT. The new encoder enriches each source state with dependencies in the tree. Then, Wu proposed a novel sequence dependence framework. In this framework, the target transformation and its corresponding dependency tree are jointly constructed and modeled. During decoding, the tree structure is used as a context to facilitate word generation [3]. However, there are many influencing factors in this research process, so there are certain differences in experimental results.


## 1.4. Innovation

The innovation of this paper is as follows: (1) Aiming at the transformer machine translation model using multihead attention mechanism and feed-forward neural network, inspired by linguistic cognition, it is proposed that, on the basis of this model, it can be integrated into the coding process of the source language by adding part-of-speech information vector representation to improve the translation effect of the transformer model. (2) Aiming at the machine translation model with a cyclic neural network as the network structure, this paper introduces another cyclic neural network variant-independent cyclic neural network as the network structure of encoder and decoder.


## 2. Neural Network-Related Technologies

The end-to-end neural machine translation uses the encoder and decoder as the framework to construct a translation model [4, 5]. Among them, some stages of the decoder need to use the column search algorithm to quickly obtain the optimal translation result. The training process of the neural network model is equivalent to the process of gradient calculation. The activation function and stochastic gradient descent method need to be used to optimize the training process. At the same time, fixed evaluation criteria are used to evaluate the performance of machine translation [6].


### 2.1. Stochastic Gradient Descent Algorithm

Stochastic gradient descent is often used in the process of neural network training to optimize the iterative effect of parameter update [7, 8]. As a general method to solve unconstrained optimization problems, gradient descent is suitable for many control variables, and the control system is more complicated. It is impossible to establish an accurate mathematical model for the optimal control process [9]. The optimal solution is obtained through successive iterations, and the gradient vector of the objective function must be solved in each step. For the linear regression model, the hypothetical function is expressed as follows:(1)hθx1,x2,…xn=θ0+θ1x1+…+θnxn,where θii=0,1,…,n is the model parameter and xii=0,1,…,n is the n feature values of each sample; adding a feature x0=1, the above formula can be transformed into(2)hθx1,x2,…xn=∑i=1nθixi.The corresponding cost function is(3)Jθ=12m∑j=0mhθx0j,x1j,...,xnj−yj2.The gradient descent algorithm is described in detail as follows:(1)
First, the relevant parameters are initialized: the size of 0.0. The learning rate is initialized, and the algorithm termination distance isε.(2)
Determine the cost function gradient of the current position. The gradient calculation formula ofθi is as follows:(4)∂∂θiJθ0,θ1,…,θn.(3)
Multiply the learning rate and the cost function gradient to obtain the descending distance of the current position.(4)
Compare the gradient descent distance of allθi with ε. If it is less than ε, the algorithm terminates, and all current θi are the final training results; otherwise, go to the next step.(5)
Update allθ as shown in the following formula. After the update is completed, return to step 2 to continue execution until the end criterion is met.(5)θi=θi−α∂∂θiJθ0,θ1,…,θn.


### 2.2. Bar Search Algorithm

As a search strategy used in the decoding stage of neural translation, the column search algorithm is a heuristic graph search algorithm used to search for the best expansion node in a limited set of graphs or trees and is often used in systems with large solution spaces [1, 10]. The search tree of the pillar search algorithm in the system is constructed using a breadth-first strategy. Sort according to the nodes on each level of the tree, keep the number of nodes consistent with the width of the column, and delete the remaining nodes. Continue to expand these nodes to the next level, and delete invalid nodes [3, 11].The background vectorC is obtained through the encoder, which contains the relevant information of the input source language sequence x=x1,x2,…,xT [12]. Assuming that the output sequence in the training data isy=y1,y2,…,yT, the probability of generating the output sequence is(6)Py1,y2,…,yT=∏t=1TPyt|y1,…,yT−1,c.Assuming that the vocabulary of the specified target language is |y|, if the output sequence length is T, the current possible output sequence types are yT. To find the output sequence with the highest generation probability, the probability of generating all possible sequences can be calculated [13]. The maximum probability sequence is the output after comparison, that is, the best sequence, but this method takes up too much time and space. Another method is to extract the most likely vocabulary created at any point in time and find the most likely result from the vocabulary of the target language defined at any point in time [14].(7)yt=argmaxyt∈yPyt|t1,…,yt−1,c.At this time, the search calculation cost is significantly reduced compared with the previous method, but the optimal sequence cannot be guaranteed. The bar search algorithm is a search strategy between these two methods [15].(8)1LαlogPy1,…,yL=1Lα∑t=1LlogPyt|t1,…,yt−1,c.Among them,L represents the length of the candidate sequence, and La penalizes the logarithmic addition in the score of the longer sequence.


### 2.3. Dropout

Overfitting is a common problem in deep neural networks. The occurrence of overfitting will cause the training speed to slow down, or the prediction result is too different from the training result. Dropout, as one of the various solutions to the overfitting problem, has the advantages of simple implementation and good effect [16, 17]. Figure 1 shows the implementation of Dropout. From this figure, we can see the comparison of the network structure of the neural network before and after the use of Dropout.Figure 1
Schematic diagram of Dropout. (a) Primitive neural network. (b) Neural network after Dropout.
(a)(b)In order to describe the image from the details of the object and at the same time describe the relationship between the gray levels in space and reflect the transformation of the object in the direction, position, and shape, the text chooses to use texture features to express this demand. The following first introduces the realization process of the gray-gradient cooccurrence matrix and the extracted variables and then explains the principle and realization method of the gray-scale cooccurrence matrix. Suppose that the input gray image isf (i, j), where i = 0, 1, 2,.., Li − 1 and j = 0, 1, 2, ..., Li  − 1, and normalize the gray image:(9)Fi,jfi,j−minfi,jmaxfi,j−minfi,jL−1.L is the highest gray level of the processed image. Suppose that the gradient image is g (i, j), and the gradient is normalized:(10)Gi,jgi,j−mingi,jmaxgi,j−mingi,jLg−1,Lg is the highest gray level of the gradient image. Gray-gradient cooccurrence matrix is as follows:(11)Hm,nm=0,1,2,…,L−1;n=0,1,2,…,Lg−1.In order to facilitate the calculation, it is necessary to normalize the gray-gradient cooccurrence matrix.Hm,n^ indicate the frequency of the normalized gray image and the gray value on the gradient image as m and n at the same time. The processed expression is as follows:(12)Hm,n^=Hm,nLiLj.Energy is as follows:(13)T1=∑m=0L−1∑n=0Lg−1Hm,n^2.Gradient average is as follows:(14)T2=∑n=0Lg∑m=0L−1Hm,n^2.Gradient mean square error is as follows:(15)T3=∑n=0Lg−1n−T22∑m=0L−1Hm,n^1/2.Moment of deficit is as follows:(16)T4=∑m=0L−1∑n=0Lg−1Hm,n^1+m−n2.In the standard neural network, each parameter determines how to update through its derivative to achieve the purpose of reducing the cost function, and the neuron also corrects the errors of other units in this way. There will be complex interconnections between neurons, but this relationship does not extend to the unknown data, which leads to the occurrence of overfitting. The main idea of Dropout is to avoid overfitting by making other hidden units unreliable. Neurons in the network are rejected with probabilityP, and the remaining neurons are retained with probability I − P. It reduces the adaptability of joints between neuron nodes and enhances their ability to generalize to the network [18].


## 2.1. Stochastic Gradient Descent Algorithm

Stochastic gradient descent is often used in the process of neural network training to optimize the iterative effect of parameter update [7, 8]. As a general method to solve unconstrained optimization problems, gradient descent is suitable for many control variables, and the control system is more complicated. It is impossible to establish an accurate mathematical model for the optimal control process [9]. The optimal solution is obtained through successive iterations, and the gradient vector of the objective function must be solved in each step. For the linear regression model, the hypothetical function is expressed as follows:(1)hθx1,x2,…xn=θ0+θ1x1+…+θnxn,where θii=0,1,…,n is the model parameter and xii=0,1,…,n is the n feature values of each sample; adding a feature x0=1, the above formula can be transformed into(2)hθx1,x2,…xn=∑i=1nθixi.The corresponding cost function is(3)Jθ=12m∑j=0mhθx0j,x1j,...,xnj−yj2.The gradient descent algorithm is described in detail as follows:(1)
First, the relevant parameters are initialized: the size of 0.0. The learning rate is initialized, and the algorithm termination distance isε.(2)
Determine the cost function gradient of the current position. The gradient calculation formula ofθi is as follows:(4)∂∂θiJθ0,θ1,…,θn.(3)
Multiply the learning rate and the cost function gradient to obtain the descending distance of the current position.(4)
Compare the gradient descent distance of allθi with ε. If it is less than ε, the algorithm terminates, and all current θi are the final training results; otherwise, go to the next step.(5)
Update allθ as shown in the following formula. After the update is completed, return to step 2 to continue execution until the end criterion is met.(5)θi=θi−α∂∂θiJθ0,θ1,…,θn.


## 2.2. Bar Search Algorithm

As a search strategy used in the decoding stage of neural translation, the column search algorithm is a heuristic graph search algorithm used to search for the best expansion node in a limited set of graphs or trees and is often used in systems with large solution spaces [1, 10]. The search tree of the pillar search algorithm in the system is constructed using a breadth-first strategy. Sort according to the nodes on each level of the tree, keep the number of nodes consistent with the width of the column, and delete the remaining nodes. Continue to expand these nodes to the next level, and delete invalid nodes [3, 11].The background vectorC is obtained through the encoder, which contains the relevant information of the input source language sequence x=x1,x2,…,xT [12]. Assuming that the output sequence in the training data isy=y1,y2,…,yT, the probability of generating the output sequence is(6)Py1,y2,…,yT=∏t=1TPyt|y1,…,yT−1,c.Assuming that the vocabulary of the specified target language is |y|, if the output sequence length is T, the current possible output sequence types are yT. To find the output sequence with the highest generation probability, the probability of generating all possible sequences can be calculated [13]. The maximum probability sequence is the output after comparison, that is, the best sequence, but this method takes up too much time and space. Another method is to extract the most likely vocabulary created at any point in time and find the most likely result from the vocabulary of the target language defined at any point in time [14].(7)yt=argmaxyt∈yPyt|t1,…,yt−1,c.At this time, the search calculation cost is significantly reduced compared with the previous method, but the optimal sequence cannot be guaranteed. The bar search algorithm is a search strategy between these two methods [15].(8)1LαlogPy1,…,yL=1Lα∑t=1LlogPyt|t1,…,yt−1,c.Among them,L represents the length of the candidate sequence, and La penalizes the logarithmic addition in the score of the longer sequence.


## 2.3. Dropout

Overfitting is a common problem in deep neural networks. The occurrence of overfitting will cause the training speed to slow down, or the prediction result is too different from the training result. Dropout, as one of the various solutions to the overfitting problem, has the advantages of simple implementation and good effect [16, 17]. Figure 1 shows the implementation of Dropout. From this figure, we can see the comparison of the network structure of the neural network before and after the use of Dropout.Figure 1
Schematic diagram of Dropout. (a) Primitive neural network. (b) Neural network after Dropout.
(a)(b)In order to describe the image from the details of the object and at the same time describe the relationship between the gray levels in space and reflect the transformation of the object in the direction, position, and shape, the text chooses to use texture features to express this demand. The following first introduces the realization process of the gray-gradient cooccurrence matrix and the extracted variables and then explains the principle and realization method of the gray-scale cooccurrence matrix. Suppose that the input gray image isf (i, j), where i = 0, 1, 2,.., Li − 1 and j = 0, 1, 2, ..., Li  − 1, and normalize the gray image:(9)Fi,jfi,j−minfi,jmaxfi,j−minfi,jL−1.L is the highest gray level of the processed image. Suppose that the gradient image is g (i, j), and the gradient is normalized:(10)Gi,jgi,j−mingi,jmaxgi,j−mingi,jLg−1,Lg is the highest gray level of the gradient image. Gray-gradient cooccurrence matrix is as follows:(11)Hm,nm=0,1,2,…,L−1;n=0,1,2,…,Lg−1.In order to facilitate the calculation, it is necessary to normalize the gray-gradient cooccurrence matrix.Hm,n^ indicate the frequency of the normalized gray image and the gray value on the gradient image as m and n at the same time. The processed expression is as follows:(12)Hm,n^=Hm,nLiLj.Energy is as follows:(13)T1=∑m=0L−1∑n=0Lg−1Hm,n^2.Gradient average is as follows:(14)T2=∑n=0Lg∑m=0L−1Hm,n^2.Gradient mean square error is as follows:(15)T3=∑n=0Lg−1n−T22∑m=0L−1Hm,n^1/2.Moment of deficit is as follows:(16)T4=∑m=0L−1∑n=0Lg−1Hm,n^1+m−n2.In the standard neural network, each parameter determines how to update through its derivative to achieve the purpose of reducing the cost function, and the neuron also corrects the errors of other units in this way. There will be complex interconnections between neurons, but this relationship does not extend to the unknown data, which leads to the occurrence of overfitting. The main idea of Dropout is to avoid overfitting by making other hidden units unreliable. Neurons in the network are rejected with probabilityP, and the remaining neurons are retained with probability I − P. It reduces the adaptability of joints between neuron nodes and enhances their ability to generalize to the network [18].


## 3. Translation Experiment

### 3.1. Experimental Setup

We apply the methods proposed in this paper to the actual Chinese-English statistical machine translation system to verify their effectiveness. The systems used in this experiment are all phrase statistical machine translation systems based on neural networks. In this experiment, we used data from the news field to verify the research on idioms. The training corpus we use in the experiment is FBIS corpus, the development set uses the test set of NIST MT 2002, the test set uses the test set of NIST MT 2005 and NIST MT 2006, and the test set is extracted from the NIST MT 2004–2006 test set. Sentences containing idioms are used as a test set, which is called NIST-Idiom in the following. The language model used in the experiment is a 4-gram language model trained on the Gigaword corpus through the SRILM tool. The word alignment tool uses GIZA++. For the experimental results, we use case-insensitive BLEU, GTM, and manual evaluation to evaluate the translation quality. The manual evaluation is based on the translation results from 0 to 5 points, and then the score of each sentence is added and divided by the total number of sentences in the test set as the score of the translation result of the test set: 3 different people scored and the average was taken as the final score. Table1 shows the experimental data we used.Table 1
Experimental data.
ChineseEnglishTraining setFBISSentence239 KDevelopment setNIST02Sentence8763520Test setNIST05Sentence10264251NIST06Sentence16655326NIST-IdiomSentence3251012Language model GigawordSentence—11.5 MNote. “K” means the unit “thousand,” and “M” means the unit “million.”This paper sets up performance test experiments for the test set retelling and replacing, respectively. The specific experimental results and analysis will be introduced in detail as follows.


### 3.2. Test Set Retelling Replacement Performance Test Experiment

Due to the relatively abundant Chinese-English resources on the Internet, we found an English-Chinese Chinese-English dictionary (73003 word pairs). In order to compare with the method of using the dictionary, we set up two baselines (BL1 and BL2). BL1 only uses FBIS for training, and BL2 uses FBIS and dictionary resources for training. We experiment Method 1 on both baselines. In the experiment, not only were the unregistered idioms in the test set replaced, but also the corresponding test ensemble paraphrase replacement comparative experiment was carried out according to the number of times the idiom appeared in the training set. We constructed the NIST-Idiom test set for testing. The experimental results are shown in Figure2.Figure 2
The results of the test set paraphrase replacement on NIST05 and NIST06.It can be seen from Figure2 that whether it is on BL1 or BL2, the use of unregistered words in the replacement test set has improved in GTM, Meteor, and manual evaluation and slightly decreased on BLEU. The reason may be that the BLEU method is based on N-ary matching, and after the replacement idiom is repeated, the replacement part is often longer than the original sentence, resulting in lower scores. It can also be seen from the experimental results that, for the idioms with the number of occurrences less than or equal to 10 in the training set, corresponding replacements in the test set will obtain the highest Meteor and manual evaluation scores.


### 3.3. Training Ensemble Paraphrase Replacement Performance Test Experiment

In this experiment, the training ensemble paraphrase replacement is used on the BL1 baseline system to test the NIST05, NIST06, and NIST-Idiom test sets. According to the number of occurrences of the idiom in the training set, we performed the paraphrase replacement of the idiom in the training set and compared the experimental results according to the different number of occurrences. The experimental results are shown in Table2.Table 2
Test results of NIST05, NIST06, and NIST-Idiom for training ensemble paraphrase replacement.
BLEUGTMMeteorArtificialNumber of replacementsBLI26.5069.6953.742.400BL226.6370.2054.122.440R126.5469.7253.752.421263R526.7370.8454.232.584676R1026.6870.5853.892.489101R2026.6570.0653.872.4714521R5026.1769.5352.882.3521275RALL26.2269.4953.322.3830125It can be seen from Table2 that although in different test sets, several evaluation methods are not completely inconsistent, the best replacement effect of each test set is not the replacement of the same frequency, but the number of occurrences in the replacement training set is less than 20. The translation results of the idioms on the three test sets have improved compared with the indicators of the baseline system. Compared with BL2 with dictionary resources, the best effect of training ensemble paraphrase replacement on the test set is better than BL2. Comparing training ensemble paraphrase replacement and test set paraphrase replacement, training ensemble paraphrase replacement improves translation model training from the perspective of the training set and improves the translation quality of the model, and test set paraphrase replacement solves the translation of unregistered idioms from the perspective of the test set. On the NIST-Idiom test set, the advantage of training ensemble paraphrase replacement in the automatic evaluation method is greater than that of the test set paraphrase replacement, and the test set paraphrase replacement has a greater advantage in manual evaluation.


## 3.1. Experimental Setup

We apply the methods proposed in this paper to the actual Chinese-English statistical machine translation system to verify their effectiveness. The systems used in this experiment are all phrase statistical machine translation systems based on neural networks. In this experiment, we used data from the news field to verify the research on idioms. The training corpus we use in the experiment is FBIS corpus, the development set uses the test set of NIST MT 2002, the test set uses the test set of NIST MT 2005 and NIST MT 2006, and the test set is extracted from the NIST MT 2004–2006 test set. Sentences containing idioms are used as a test set, which is called NIST-Idiom in the following. The language model used in the experiment is a 4-gram language model trained on the Gigaword corpus through the SRILM tool. The word alignment tool uses GIZA++. For the experimental results, we use case-insensitive BLEU, GTM, and manual evaluation to evaluate the translation quality. The manual evaluation is based on the translation results from 0 to 5 points, and then the score of each sentence is added and divided by the total number of sentences in the test set as the score of the translation result of the test set: 3 different people scored and the average was taken as the final score. Table1 shows the experimental data we used.Table 1
Experimental data.
ChineseEnglishTraining setFBISSentence239 KDevelopment setNIST02Sentence8763520Test setNIST05Sentence10264251NIST06Sentence16655326NIST-IdiomSentence3251012Language model GigawordSentence—11.5 MNote. “K” means the unit “thousand,” and “M” means the unit “million.”This paper sets up performance test experiments for the test set retelling and replacing, respectively. The specific experimental results and analysis will be introduced in detail as follows.


## 3.2. Test Set Retelling Replacement Performance Test Experiment

Due to the relatively abundant Chinese-English resources on the Internet, we found an English-Chinese Chinese-English dictionary (73003 word pairs). In order to compare with the method of using the dictionary, we set up two baselines (BL1 and BL2). BL1 only uses FBIS for training, and BL2 uses FBIS and dictionary resources for training. We experiment Method 1 on both baselines. In the experiment, not only were the unregistered idioms in the test set replaced, but also the corresponding test ensemble paraphrase replacement comparative experiment was carried out according to the number of times the idiom appeared in the training set. We constructed the NIST-Idiom test set for testing. The experimental results are shown in Figure2.Figure 2
The results of the test set paraphrase replacement on NIST05 and NIST06.It can be seen from Figure2 that whether it is on BL1 or BL2, the use of unregistered words in the replacement test set has improved in GTM, Meteor, and manual evaluation and slightly decreased on BLEU. The reason may be that the BLEU method is based on N-ary matching, and after the replacement idiom is repeated, the replacement part is often longer than the original sentence, resulting in lower scores. It can also be seen from the experimental results that, for the idioms with the number of occurrences less than or equal to 10 in the training set, corresponding replacements in the test set will obtain the highest Meteor and manual evaluation scores.


## 3.3. Training Ensemble Paraphrase Replacement Performance Test Experiment

In this experiment, the training ensemble paraphrase replacement is used on the BL1 baseline system to test the NIST05, NIST06, and NIST-Idiom test sets. According to the number of occurrences of the idiom in the training set, we performed the paraphrase replacement of the idiom in the training set and compared the experimental results according to the different number of occurrences. The experimental results are shown in Table2.Table 2
Test results of NIST05, NIST06, and NIST-Idiom for training ensemble paraphrase replacement.
BLEUGTMMeteorArtificialNumber of replacementsBLI26.5069.6953.742.400BL226.6370.2054.122.440R126.5469.7253.752.421263R526.7370.8454.232.584676R1026.6870.5853.892.489101R2026.6570.0653.872.4714521R5026.1769.5352.882.3521275RALL26.2269.4953.322.3830125It can be seen from Table2 that although in different test sets, several evaluation methods are not completely inconsistent, the best replacement effect of each test set is not the replacement of the same frequency, but the number of occurrences in the replacement training set is less than 20. The translation results of the idioms on the three test sets have improved compared with the indicators of the baseline system. Compared with BL2 with dictionary resources, the best effect of training ensemble paraphrase replacement on the test set is better than BL2. Comparing training ensemble paraphrase replacement and test set paraphrase replacement, training ensemble paraphrase replacement improves translation model training from the perspective of the training set and improves the translation quality of the model, and test set paraphrase replacement solves the translation of unregistered idioms from the perspective of the test set. On the NIST-Idiom test set, the advantage of training ensemble paraphrase replacement in the automatic evaluation method is greater than that of the test set paraphrase replacement, and the test set paraphrase replacement has a greater advantage in manual evaluation.


## 4. Case Analysis

This paper takes the application of the multisequence coding method in attention-based neural machine translation as an example and compares and analyzes the translation of the actual test sentence obtained by the multisequence coding method and the baseline method to illustrate the effectiveness of the multisequence coding method. The English automatic translation system based on machine intelligent translation and secure Internet of Things designed and implemented in this paper mainly includes a preprocessing module, an encoding and decoding module, and an attention module. The system framework is shown in Figure3:Figure 3
System architecture diagram.As shown in Figure3, the modules are preprocessing module, encoding module, attention module, and decoding module according to the order of system implementation. The four modules work together to realize the end-to-end neural machine translation process.


### 4.1. Sentence Length Sensitivity

One of the main characteristics of the end-to-end neural machine translation method is that it is sensitive to sentence length; that is, as the length of the sentence increases, the quality of the translation shows a significant downward trend. This paper takes the application of multisequence coding in traditional neural machine translation systems as an example to examine how sensitive the multisequence coding method is to sentence length. In this paper, the source language sentences in the test data set are tested according to the sentence length distribution, and the translation is independently evaluated to compare the sensitivity of the multisequence coding method and the traditional method to sentence length. Table3 shows the translation BELU scores of the baseline system and the multisequence coding system on different sentence length distributions of the test data.Table 3
Sentence length sensitivity of multisequence coding and baseline method.
Sentence lengthBaseline38.6522.5319.5813.25seq + pos38.6222.7520.3218.52seq + hypernym39.5623.0119.3615.26seq + pos + hypernym41.2323.2520.3620.55As shown in Table2, when the sentence length exceeds 20, the translation quality of the baseline method and the multisequence coding method shows a significant downward trend. Among them, the BLEU scores of translations obtained by the baseline method, seq + pos method, seq + head method, and seq + pos + head method decreased by 16.07, 15.91, 16.07, and 18.30, respectively. Looking at the overall trend, as the length of the test sentence increases, the multisequence coding method improves the translation performance on the basis of the baseline method.


### 4.2. Analysis of BLEU Translation Model

The improvement ratio represents the improvement ratio of the translation effect of the four-layer network structure when the same neural network is used in each group of experiments compared to the two-layer network structure.From the experimental results in Table4, it can be seen that, overall, the performance of the independent recurrent neural network on the translation task is better than the other three control groups. Regardless of whether the number of network layers is two or four, the effect of GRNN in the three baseline models is better than that of the other three models. With the superimposition of the number of network layers from two to four layers, the improvement ratios of each model are 5.90%, 6.1%, 6.0%, and 7.0% respectively. Among them, the model with an independent recurrent neural network as the network structure has the largest increase rate, and the greater the number of network layers, the higher the increase rate. It can be seen that the BLEU score value of the machine translation model proposed in this paper with the independent recurrent neural network as the encoder and decoder neural network is higher than that of the other three baseline models. At the same time, after stacking more layers, the effect is improved. The other three baseline models are more obvious. This shows that, after the introduction of the independent recurrent neural network, not only has the effect of the entire translation model been improved, but also the effect of this model after stacking multiple layers is more obvious than the other three models, indicating that this model is more suitable for overlaying multilayer networks.Table 4
Experimental results.
Experimental modelBLEU scoreIncrease ratio (%)1RNN-220.565.9RNN-422.252LSTM-225.326.1LSTM-425.813GRNN-225.636.0GRNN-429.624indRNN-228.527.0indRNN-430.25


## 4.1. Sentence Length Sensitivity

One of the main characteristics of the end-to-end neural machine translation method is that it is sensitive to sentence length; that is, as the length of the sentence increases, the quality of the translation shows a significant downward trend. This paper takes the application of multisequence coding in traditional neural machine translation systems as an example to examine how sensitive the multisequence coding method is to sentence length. In this paper, the source language sentences in the test data set are tested according to the sentence length distribution, and the translation is independently evaluated to compare the sensitivity of the multisequence coding method and the traditional method to sentence length. Table3 shows the translation BELU scores of the baseline system and the multisequence coding system on different sentence length distributions of the test data.Table 3
Sentence length sensitivity of multisequence coding and baseline method.
Sentence lengthBaseline38.6522.5319.5813.25seq + pos38.6222.7520.3218.52seq + hypernym39.5623.0119.3615.26seq + pos + hypernym41.2323.2520.3620.55As shown in Table2, when the sentence length exceeds 20, the translation quality of the baseline method and the multisequence coding method shows a significant downward trend. Among them, the BLEU scores of translations obtained by the baseline method, seq + pos method, seq + head method, and seq + pos + head method decreased by 16.07, 15.91, 16.07, and 18.30, respectively. Looking at the overall trend, as the length of the test sentence increases, the multisequence coding method improves the translation performance on the basis of the baseline method.


## 4.2. Analysis of BLEU Translation Model

The improvement ratio represents the improvement ratio of the translation effect of the four-layer network structure when the same neural network is used in each group of experiments compared to the two-layer network structure.From the experimental results in Table4, it can be seen that, overall, the performance of the independent recurrent neural network on the translation task is better than the other three control groups. Regardless of whether the number of network layers is two or four, the effect of GRNN in the three baseline models is better than that of the other three models. With the superimposition of the number of network layers from two to four layers, the improvement ratios of each model are 5.90%, 6.1%, 6.0%, and 7.0% respectively. Among them, the model with an independent recurrent neural network as the network structure has the largest increase rate, and the greater the number of network layers, the higher the increase rate. It can be seen that the BLEU score value of the machine translation model proposed in this paper with the independent recurrent neural network as the encoder and decoder neural network is higher than that of the other three baseline models. At the same time, after stacking more layers, the effect is improved. The other three baseline models are more obvious. This shows that, after the introduction of the independent recurrent neural network, not only has the effect of the entire translation model been improved, but also the effect of this model after stacking multiple layers is more obvious than the other three models, indicating that this model is more suitable for overlaying multilayer networks.Table 4
Experimental results.
Experimental modelBLEU scoreIncrease ratio (%)1RNN-220.565.9RNN-422.252LSTM-225.326.1LSTM-425.813GRNN-225.636.0GRNN-429.624indRNN-228.527.0indRNN-430.25


## 5. Conclusions

With the development of Internet technology, exchanges between different countries and industries have become more frequent. Language is the carrier of communication between people, and the conversion and transmission of information between different languages are very important. As an effective tool for language conversion, automatic translation can perform equivalent conversions in different languages while retaining the original semantics. This is very important in practice. The development of technologies related to deep learning has also improved the method and efficiency of machine translation. Automatic translation has gone through a process from rule-based to statistics to neuron evolution. This paper focuses on Chinese and English translation engineering and discusses the combination of deep learning technology and machine translation issues. For neural machine translation, an end-to-end neural network model framework is created, which covers the entire translation process and performs end-to-end machine translation. Both the encoder and decoder are constructed by neural networks, and words are used as distributed representation vectors. This method establishes a series of source and target languages to verify the analysis and improvement methods. The reason for this should not be limited to training parameters, and follow-up research should strive to improve the structure of the neural network to improve performance.



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*Source: 8670739-2021-07-07.xml*