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Application of Discriminative Training Algorithm Based on the Improved Gaussian Mixture Model in English Translation Evaluation
Application of Discriminative Training Algorithm Based on the Improved Gaussian Mixture Model in...
Hindawi International Transactions on Electrical Energy Systems Volume 2022, Article ID 8021240, 11 pages https://doi.org/10.1155/2022/8021240 Research Article Application of Discriminative Training Algorithm Based on the Improved Gaussian Mixture Model in English Translation Evaluation Shengnan Wang Surrey International Institute, Dongbei University of Finance and Economics, Dalian 116025, Liaoning, China Correspondence should be addressed to Shengnan Wang; email@example.com Received 30 June 2022; Revised 19 July 2022; Accepted 2 August 2022; Published 31 August 2022 Academic Editor: Raghavan Dhanasekaran Copyright © 2022 Shengnan Wang. ,is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. English translation, also a kind of language conversion, refers to the activities of expressing each other in English to express another language or another language to express English. ,is paper aimed to evaluate and research methods of English translation based on the improved Gaussian mixture model (GMM). ,e paper proposed a simple evaluation and analysis of English translation using a discriminative training algorithm. In the experimental part, on the one hand, taking students, teachers, and translators as case study objects, through a questionnaire survey, it can be known that the number of students who scored more than 80 points in the English translation test is increasing every year, from 11.30% in the ﬁrst year to 21.13% in the fourth year, an increase of nearly 10%. On the other hand, it can be seen from the speech translation experiment that the correct translation rate was 46.18% by using intelligent technology to distinguish translation. ,e experimental results showed that the discriminative training algorithm under the improved GMM algorithm is eﬀective in the research of English translation evaluation. GMM discriminative training algorithm to conduct evalua- 1. Introduction tion research in English translation. In the experimental part, the model was explored in depth, and the research results Due to the continuous progress of science and technology, show that the method is eﬀective in the experiment. the English translation evaluation system based on artiﬁcial intelligence has been widely used in English teaching and testing. ,e computer gives the learners the objective scores 2. Related Work of English translation, so as to know whether their own English translation is standard. English translation assess- In recent years, with the continuous development of the ment is a wide-ranging research topic, including the learning economy, frequent trade exchanges between enterprises in mode of translation and the selection of teaching materials various countries, and a large population ﬂow, the market for translation. ,is paper used the GMM to study English demand for English translation has continued to rise. translation assessment, which is helpful to understand the ,rough Pan’s research, using data from the Translation current social situation. It can help students to remove Corpus, a corpus of learners developed for the study of learning obstacles and help teachers to establish a correct lexical cohesion, through case studies, the results showed the translation teaching concept. complexity of a learner’s language and its relationship to ,is paper mainly studied the evaluation of English various factors such as learner, text, and task . ,rough translation by mathematical models, which has a huge pos- Li’s research, some advantages of Hu’s ecological translation itive eﬀect on the ﬁeld of English translation in the future. ,e theory were explained, the translation was used to minimize innovation of this paper is mainly based on the improved translation problems, and corpus linguistics method was 2 International Transactions on Electrical Energy Systems used, which excels in quantitative and qualitative analysis English translation evaluation refers to the test and score . Zhao et al. aimed to explore how narrative space can be after language translation. It is a speciﬁc expression of transferred from one language to another. Studies have translation ability by numbers or other standards that people shown that selective appropriation is the most commonly can generally understand, and it has scoring standards . used framing strategy . Si and Wang aimed to apply Translation assessment is required in many cases, such as grammatical metaphors from systematic functional lin- translation majors and employees engaged in translation guistics to translation studies. In order to achieve the work, both need to assess English translation ability. Figure 2 purpose of translation, an accurate and appropriate method shows scenes related to English translation evaluation. must be selected . ,rough Tan Z’s and Ke research, the purpose was to analyze the formula of English-Chinese 3.2. Gaussian Mixture Model (GMM). Gaussian mixture translation thinking mode and explore the possible ad- model (GMM) aims to use Gaussian probability density vantages in translation practice . However, due to the function to quantify things accurately . ,e features of extensive use of language, English can be translated into each pixel in the image are represented by K Gaussian many languages, and relevant mathematical models are used models. After a new frame of image is obtained, the Gaussian for statistical analysis, and the above studies are all in-depth mixture model is updated, and each pixel in the current analysis of this. image is matched with the Gaussian mixture model. Figure 3 With the development of science and technology, is a GMM diagram. Gaussian mixture model is applied in many ﬁelds, and many GMM can be regarded as a continuous hidden Markov scholars have studied it. ,rough Yang J et al.’s research, model (CHMM) with a state of 1. It can better display the based on GMM, an eﬃcient GNH approximation method probability distribution of multi-category observation data was proposed and used as a preconditioner for the mismatch in the sample space using GMM . ,e probability density gradient to speed up its convergence . ,rough the re- function of a K-order GMM is obtained by the weighted search of Gao G et al., a new method was proposed to in- summation of K Gaussian probability density functions: crease the complexity by adding a large number of Gaussian components and then check the accuracy of the GMM P(M|λ) � w b (M). (1) approximation of PDF . ,rough Xu Y et al.’s research, i i i�1 the Gaussian mixture model was used for state estimation, and the main problem was that the number of Gaussian In (1), M represents a D-dimensional random vector; components increases exponentially . After Han J et al.’s b (M), i � 1, 2, ..., K, is the sub-distribution; and w is the i i analysis, in order to obtain the eﬀective user’s actual mixing weight. Each sub-distribution is a D-dimensional emotional state and promote a harmonious human-machine joint Gaussian probability distribution, which can be interactive experience, combined with emotional space and expressed as follows: personality theory, an incremental emotion mapping model 1 1 −1 based on Gaussian mixture model was proposed . b (M) � exp− M − η M − η . i i i i D/2 1/2 2 (2π) ,rough the research of Sagratella S, a new algorithm was proposed to calculate the approximate equilibrium of (2) generalized latent games with mixed integer variables. ,e Among them, η is the mean vector, is the covariance behavior of approximate equilibrium relative to exact i t matrix, (M − η ) is the transpose of the vector (M − η ), equilibrium was analyzed, and ﬁnally, the eﬀectiveness of the i i −1 | | is the determinant, and is the inverse of the matrix method was demonstrated through numerous numerical . ,e mean vector η is the expected value of the ei- experiments . However, the above scholars have studied t i genvector M, and the covariance matrix represents the the original GMM and have not conducted a thorough study cross-correlation and variance of the eigenvector elements. of the improved GMM. Let 3. Method of English Translation Assessment K w � 1. (3) Based on Discriminative Training Algorithm i�1 3.1. English Translation Assessment. English translation re- ,is ensures that the mixing density can represent a true fers to a way of language conversion, which includes Chi- probability density function . ,erefore, the parameter nese-English translation, Japanese-English translation, set of the complete model θ contains the GMM mean η English-Korean translation, and translation between English covariance , and weight w ; namely, t i and other languages . English translation also includes some translation skills, such as two basic methods of literal θ � w , η , , i � 1, 2, ..., K. (4) i i t translation and meaning, as well as other techniques such as provincial translation, positive translation, and reverse GMM can be explained; it is a functional expression of translation. English translation can be used in many occa- probability density function. As a linear combination of sions, such as warning signs and learning courseware. ,e Gaussian probability density functions, GMM can ap- speciﬁc situation is shown in Figure 1, which is an illus- proximate any density function as long as there are a suf- tration related to English translation. ﬁcient number of mixed components . Language International Transactions on Electrical Energy Systems 3 Figure 1: Diagrams related to English translation. Figure 2: Scenarios related to English translation assessment. translation usually has a smooth probability density func- parameter estimates of the GMM in iterations, increasing the tion, so a ﬁnite Gaussian density function is suﬃcient to matching probability of the model estimate θ and the observed form a smooth approximation to the density function of feature vector at each iteration, and at each iteration, there is k+1 k English translation features . P(|M|θ )> P(|M|θ ), where k is the number of iterations. ,e method used to estimate the GMM parameters is the Figure 4 is a step diagram of the expectation maximi- maximum likelihood estimation method; that is, with respect zation algorithm. to θ, the conditional probability P(M|θ) is maximized . It is assumed that feature vectors are independent of each ,e expectation maximization (EM) algorithm improves the other; there are 4 International Transactions on Electrical Energy Systems 300 w b M i t t P i|M , θ � . (11) w b M j�1 j j t ,en, we change the symbols in (8), (9), (10), and (11), and the iterative algorithm expression of θ can be obtained as follows: T−1 k+1 k w � P i||M , θ , (12) i t t�0 T−1 k P i|M , θ M t�0 t t k+1 η � , (13) T−1 k Pi|M , θ t�0 t T−1 k T k+1 Pi|M , θ