Neural network algorithms have diverse characteristics, and as an essential learning method, a deep neural network is widely used in many fields, such as machine learning. Its most significant advantage is that it has a high degree of conformity with the human cognitive process. Its widely used neural network algorithm has potent functions. Its essence can be simplified into a multi-layer neural network. With the continuous development of science and technology, deep neural network algorithms have been favored by algorithm researchers in many fields such as natural language and applied in practice. The specific analysis algorithms included in the deep neural network include question answering analysis, dependency, and syntax analysis. This research uses the advantages of a deep neural network. It extends it to the mathematical modeling process of English-Chinese translation, which is used for English Chinese translation organization learning. So, specifically, this neural network contains the main structures of vocabulary, alignment, language, warping, translation model, etc. Deep neural networks are applied in natural language, and their processing is essentially embedded or represented in the form of words. The word embedding refers to the low-dimensional, high-density, and real-valued vectors to reflect the sentence meaning it contains. In this model, the deep neural network algorithm gives full play to its advantages and fully utilizes its benefits in transforming feature extraction methods. The traditional algorithm is mainly based on the manual extraction of features, while the deep neural network evolves it into automatic extraction. Therefore, compared with manual extraction, it breaks the inherent limitations and breaks through the shortcomings of incomplete information in manual feature extraction. In addition, after the eigenvalue extraction is completed, the deep neural network algorithm will participate in the eigenvalue classification or eigenvalue regression in the form of a classifier or regressor and ensure that the features after classification or regression exist in the same continuous spatial range. Therefore, it is beneficial to measure the relationship of the eigenvalues of this range space.
The mathematical model of English-Chinese translation constructed in this research, the neural network algorithm, is introduced in the training stage. Then the feature value extraction is performed to complete the decoding process to reduce the difficulty of prolonging the time caused by the complexity of decoding. This process is to divide the complexity effectively. And effectively reduce the complexity during the training phase. This research introduces a more optimized and robust functional model, that is, a translation model with hierarchical features to complete the English-Chinese translation mathematical model, and incorporates the feature differences between the original target etymology and the target language during the model building so that it can be Optimize the translation model. Secondly, the English-Chinese translation model featuring a hierarchical translation model has more substantial generalization power than general models, breaks through the limitations of available mathematical models, and can be widely used in various contexts, including classic phrases Or in the translation of words, and also in parenthetical transcription.
The English-Chinese translation mathematical model constructed in this subject has been built with componentized thinking. The advantage of this thinking is that it can measure the role of each component during the completion of the translation function from a better perspective. When the model is trained, the standard training method can be used, and the typical training method can measure each component. The function that the part performs during translation (
Figure 1 shows a schematic diagram of the overall framework of deep neural network training. As can be seen from the figure, the data domain processing is divided into two processes single-statement and the double-statement preprocessing process. When data preprocessing is performed, several phrases or rules are selected. The trained neural network model is used to generate the corresponding word vector in this subject. The word vector is sent to the HRNN system as an input parameter to complete the learning and training of the process. It should be noted here that when performing the training process of the neural network, it must be ensured that the number of training layers has the same height as the number of derivations generated by the sentence.
Preprocessing is to generate word vectors after completing the extraction of phrases and rules (
We use a total of 3 deeply recursive encoders. Recursive encoders for different target language structures are generated based on the word alignment results in the phrases . The semantic vector recovered from the encoding part is denoted as
The English-Chinese translation mathematical model built in this research introduces three encoders, among which the autoencoder has two characteristics, the first is a phrase-based word encoder, and the second is a rule-based word encoder, each part contains a lot of layers, so it is necessary to first train the word-phrase and rule-based word encoders separately, and then combine them to complete the joint training. For example, a phrase-based autoencoder is trained as follows
The hierarchical unsupervised pre-training stage needs to obtain word vectors. Word vectors exist in a matrix
The encoding layer and decoding layer use different matrices for linear transformation. The activation function of the neural network adopts the “sigmoid” function. The formula used by the function of the encoding layer is as follows:
The formula used by the decoding layer is as follows:
The English-Chinese translation mathematical model built in this research has the reconstruction error of self-encoding, so the norm of the vector is generally used to model the error. The following expressions (5) and (6) represent the functional expressions of word reconstruction and partial phrase reconstruction errors, respectively. The biggest difference between word reconstruction errors and phrase reconstruction errors is the length, because the difference in length will cause the reconstruction to affect the difference. Therefore, when using expression (6) to model short and reconstructed errors, it is necessary to obtain the weight of each reconstructed part based on the length.
In formula (5), a coefficient is added in front of the norm for the convenience of derivation. In formula (6) |
The reconstruction error of the source phrase is denoted as
We employ different encoding and decoding projection matrices to distinguish between phrases and rules and source and target sides. Θ
We need to consider the correction of the phrase/rule semantic vector by the autoencoder between the source and target languages. The transformation matrices used for source/target language side phrases are
The formula for the bilingual phrase/rule (
The semantic vectors of the source language side and the target language side corresponding to the bilingual phrase (
The parameter estimation stage consists of three training parts. The first training stage is word vector training in an unsupervised context, the second stage is monolingual phrase training and rule training in an unsupervised context, and the third stage is supervised context Bilingual Phrase Training and Rule Training under. The estimation of the parameter Θ
Equation (15) represents the partial derivative of the phrase reconstruction error on the original language side the parameter
The estimation of the parameter Θexp in the objective function
Decoding integration is to integrate semantic features into the decoder. This paper sets the cooccurrence number
The two monolingual semantic features of the source language and the target language are denoted as
The evaluation of the target language side short sentence sensitivity feature and the target language rule semantic sensitivity feature has the same form as Equation (19). In this subject
Considering the influence of semantic features, this research adopts two similar baseline systems. First, there are baseline1 baseline systems without semantic features and deadline2 baseline systems without hierarchical model construction, mainly to measure the influence of translation performance by a semantic vector and evaluate the utility of the hierarchical neural network in this model. Bassline 2, which is not built with a hierarchical model, is introduced. The implementation process is similar to HRNN, except that the impact of sentence alignment is not considered too much during training, and it is applied to the phrase hierarchy and rule hierarchy of the original language and the target language. Process. The construction direction of the semantic vector on the original sentence side and the target language side used in this paper is from left to right. It is guaranteed that the baseline system baseline2 built without using the hierarchical model must contain the similarity of bilingual semantics.
This research pioneered the introduction of bilingual features, namely bass and bass, representing bilingual semantic similarity and sensitivity features. In addition, the monolingual features mssm and men are also considered in the model, which represent monosemantic similarity and sensitivity features, respectively.
Effects of different semantic features
We design experiments where the dimension of the semantic vector takes different values n=50, 100, and 200. n=50 is the result in HRNN(bssm+bssn+mssm+mssn) in Table 1. Regardless of the importance of n, the HRNN model with bilingual and monolingual semantic features outperforms the baseline systems baseline1 and baseline2. At the same time, it achieves the best performance at n=100.
Effect of semantic vector dimension on translation performance
When analyzing the influence of binary bifurcation of rules, the number of non-terminal symbols is limited to two to four. In processing, the number of differentiated non-terminal characters is normalized, and finally, two non-terminal marks are formed. Terminal symbol, and do the same projection matrix processing. Since all the rules in this model can be binarized, a small number of 3.24% binarized rules are filtered out, and the removed function is used to perform this function. And use binarization to complete the binary processing of the rules. The results are shown in Table 4. The effect of binary on the English-Chinese translation performance can be seen from the figure.
In this research, a mathematical model of English-Chinese translation is built, and a deep learning algorithm is introduced to perform a hierarchical recursive analysis of the model to optimize the performance. The built model is more in line with the actual translation understanding process. The model incorporates global information Consideration of word vectors and bilingual alignment information is also included during neural network training. In addition, three training modules are introduced in the eigenvalue training process. Different training uses different objective functions better to reduce the impact of the interference balance module, and hierarchical pre-training is used in unsupervised situations to minimize the effect. Training time complexity improves training speed. And use bilingual features and monolingual features to filter the training data, in addition, to test the validity of the data, through example demonstration research found that the English-Chinese translation model built in this topic is superior to the classic baseline system, mainly in BLEU In terms of scores, it is about 1.84 BLEU scores higher than the baseline system.
Effects of different semantic features
Effect of semantic vector dimension on translation performance
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