English translation management system based on artificial intelligence

Abstract

The invention discloses an English translation management system based on artificial intelligence. The system comprises a translation corpus acquisition module, a translation confidence interval construction module, a translation quality loss function construction module, a perception constraint coefficient mapping module, a translation result discrimination model construction module, a translation result discrimination model training module and an English translation management module. The invention belongs to the field of translation management, and particularly relates to an English translation management system based on artificial intelligence. According to the scheme, smooth amplitude limiting and sentence pattern grading constraint are achieved by designing a translation quality loss function, and extreme noise dominance is limited; all samples are subjected to hierarchical constraint through perception constraint coefficient mapping; based on self-adaptive weighted fluctuation wave suppression gradient optimization, it is ensured that professional low-frequency translation errors obtain larger gradient response and stronger punishment, the management requirement for large term mistranslation influence in a professional English translation scene is met, and the discrimination ability of low-frequency professional translation is improved; and the English translation management effect is improved.

Description

Technical Field

[0001] This invention relates to the field of translation management, specifically to an English translation management system based on artificial intelligence. Background Technology

[0002] An English translation management system refers to a software platform or technology system used to organize, execute, monitor, and optimize cross-language translation processes, aiming to improve translation efficiency, consistency, and delivery quality. However, general English translation management systems suffer from several drawbacks: they fail to adapt to sentence structure differences and are poorly suited to specialized scenarios; extreme noise reduces overall discrimination capabilities, failing to highlight the severity of low-frequency professional translation errors, thus leading to poor translation management reliability; and they are often hampered by a large number of low-quality translation samples, causing the discrimination boundary to be biased towards noise, affecting the accuracy of real translation evaluation, and neglecting low-frequency professional translations, ultimately resulting in poor translation management effectiveness. Summary of the Invention

[0003] To address the aforementioned issues and overcome the shortcomings of existing technologies, this invention provides an AI-based English translation management system. Addressing the problems of general English translation management systems failing to adapt to sentence structure differences, exhibiting poor adaptability to specialized scenarios, and suffering from low-frequency professional translation errors due to extreme noise reducing overall discrimination ability and failing to highlight the severity of such errors, thus leading to poor translation management reliability, this solution quantifies deviation levels by defining translation confidence intervals, supporting differentiated processing of minor deviations and serious errors. It also designs a translation quality loss function to achieve smoothing and grading constraints on sentence structure, limiting the dominance of extreme noise. Furthermore, it uses perceptual constraint coefficient mapping to ensure all samples are subject to grading constraints, with higher constraint weights for professional samples, thereby improving... This solution enhances the reliability of English translation management. Addressing the issues of general English translation management systems being hampered by a large number of low-quality translation samples, biased judgment boundaries leading to noise, and insufficient attention to professional low-frequency translations, resulting in poor translation management effectiveness, this solution introduces a translation quality loss function and sentence type prediction loss. It designs an objective function to achieve asymmetric, constrained, and unbalanced perception, making it more adaptable to complex semantics. Based on adaptive weighted volatility suppression gradient optimization, it ensures that professional low-frequency translation errors receive a larger gradient response and stronger penalty, meeting the management needs of professional English translation scenarios where terminology mistranslation has a significant impact, and improving the judgment ability of low-frequency professional translations; thereby improving the overall effectiveness of English translation management.

[0004] The technical solution adopted by the present invention is as follows: The English translation management system based on artificial intelligence provided by the present invention includes a translation corpus acquisition module, a translation confidence interval construction module, a translation quality loss function construction module, a perceptual constraint coefficient mapping module, a translation result discrimination model construction module, a translation result discrimination model training module, and an English translation management module;

[0005] The translation corpus acquisition module obtains bilingual parallel corpora, labels them with translation quality and sentence type tags, and constructs a corpus set.

[0006] The translation confidence interval construction module constructs translation confidence intervals that characterize the degree of translation deviation;

[0007] The translation quality loss function construction module is designed to differentiate between low-frequency specialized corpora and high-frequency general corpora.

[0008] The perceptual constraint coefficient mapping module maps sentence categories into category-level constraints based on the corpus set, sentence category labels, and balance coefficients.

[0009] The translation result discrimination model construction module is designed with a four-layer end-to-end architecture, embedding a translation quality loss function to construct a translation result discrimination model.

[0010] The translation result discrimination model training module performs gradient optimization on the translation result discrimination model to complete the model training;

[0011] The English translation management module implements English translation management based on a trained translation result discrimination model.

[0012] Furthermore, the translation confidence interval construction module constructs a translation confidence interval, which characterizes the degree of deviation between the translation result and the standard translation.

[0013] Furthermore, the translation quality loss function construction module specifically includes:

[0014] The basic smoothing framework design limits the smoothing of losses that grow arbitrarily.

[0015] The translation quality loss function is designed to assign independent constraint strengths to low-frequency specialized corpora and high-frequency general corpora, thereby strengthening the constraint on low-frequency mistranslations.

[0016] Theoretical constraints are established, and the upper limit of loss is fixed.

[0017] Furthermore, the perception constraint coefficient mapping module directly maps sentence categories to constraint coefficients, thereby applying category-based constraints to all samples.

[0018] Furthermore, the translation result discrimination model construction module specifically includes:

[0019] The model architecture is designed as a four-layer end-to-end architecture for translation result discrimination, consisting of a feature layer, a discrimination layer, a loss layer, and a constraint optimization layer. Based on a corpus, it receives source language features and translation features as joint input. The feature layer fuses and encodes the bilingual features to extract source-translation matching semantic features. The discrimination layer outputs in parallel: translation quality confidence and sentence category prediction. The loss layer takes the confidence interval as input and uses the weighted sum of the translation quality loss function and the sentence category prediction loss term as the model's total loss. The constraint optimization layer minimizes the objective function and updates the parameters using a volatility-suppressing gradient algorithm.

[0020] Construct an objective function to directly manage translation features and confidence levels end-to-end in the original feature space, thereby obtaining the final optimization objective of the model.

[0021] Furthermore, the translation result discrimination model training module adopts adaptive weighted volatility suppression gradient optimization of the translation result discrimination model and introduces domain type weights; thereby completing the model training.

[0022] Furthermore, the English translation management module embeds the trained model into the translation management process, calculates the translation quality confidence score, and makes category-adaptive translation decisions.

[0023] The beneficial effects achieved by the present invention using the above solution are as follows:

[0024] (1) In view of the problems that general English translation management systems have poor adaptability to professional scenarios due to unsuitable sentence structure differences, extreme noise reduces the overall discrimination ability and fails to highlight the severity of professional low-frequency translation errors, thus leading to poor reliability of translation management, this solution quantifies the degree of deviation by defining translation confidence intervals to support differentiated processing of minor deviations and serious errors; it achieves smooth amplitude limiting and sentence structure hierarchical constraints by designing translation quality loss function to limit the dominance of extreme noise; and it achieves hierarchical constraints for all samples through perceptual constraint coefficient mapping, with higher constraint weights for professional samples; thereby improving the reliability of English translation management.

[0025] (2) To address the problems of general English translation management systems being dragged down by a large number of low-quality translation samples, biased discrimination boundaries due to noise, and inadequate attention to professional low-frequency translation, which leads to poor translation management results, this solution introduces a translation quality loss function and sentence type prediction loss to design an objective function, achieving asymmetric, restricted, and unbalanced perception, which is more adaptable to complex semantics; based on adaptive weighted volatility suppression gradient optimization, it ensures that professional low-frequency translation errors receive a larger gradient response and stronger penalty, which meets the management needs of professional English translation scenarios where terminology mistranslation has a significant impact, and improves the discrimination ability of low-frequency professional translation; thereby improving the English translation management effect. Attached Figure Description

[0026] Figure 1 This is a flowchart illustrating the AI-based English translation management system provided by the present invention.

[0027] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. Detailed Implementation

[0028] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0029] In the description of this invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0030] Example 1, see Figure 1 The present invention provides an AI-based English translation management system, which includes a translation corpus acquisition module, a translation confidence interval construction module, a translation quality loss function construction module, a perceptual constraint coefficient mapping module, a translation result discrimination model construction module, a translation result discrimination model training module, and an English translation management module.

[0031] The translation corpus acquisition module obtains bilingual parallel corpora, labels them with translation quality and sentence type tags, constructs a corpus set, and sends the data to the translation confidence interval construction module.

[0032] The translation confidence interval construction module constructs a translation confidence interval that characterizes the degree of translation deviation; and sends the data to the translation quality loss function construction module;

[0033] The translation quality loss function construction module is designed to distinguish between low-frequency professional corpora and high-frequency general corpora; and sends the data to the perceptual constraint coefficient mapping module.

[0034] The perceptual constraint coefficient mapping module maps sentence categories to category-level constraints based on the corpus set, sentence category labels, and balance coefficients; and sends the data to the translation result discrimination model construction module.

[0035] The translation result discrimination model construction module is designed with a four-layer end-to-end architecture, embeds a translation quality loss function, constructs a translation result discrimination model, and sends data to the translation result discrimination model training module.

[0036] The translation result discrimination model training module performs gradient optimization on the translation result discrimination model to complete the model training; and sends the data to the English translation management module.

[0037] The English translation management module implements English translation management based on a trained translation result discrimination model.

[0038] Example 2, see Figure 1 This embodiment is based on the above embodiment. The translation corpus acquisition module acquires bilingual parallel corpora (Chinese-English) and labels each sample with: translation quality tags (whether it is noise / misalignment / low quality) and sentence type tags (general high frequency / professional low frequency); and defines the corpus set. , represented as: ;in, It is a feature of the source language (Chinese) (word vectors); These are the features (word vectors) of the translated text (English). It is a translation quality label; Indicates a high-quality standard sample. This indicates a low-quality, noisy sample. It is a sentence type category marker. Indicates low-frequency professional category, Represents a high-frequency general-purpose class; is the total number of parallel corpora, and i is the index of the parallel corpus; bilingual parallel corpora are pairs of data in the source language (Chinese) and the translated language (English); word vectors are obtained by training a distributed text representation using the Word2Vec model.

[0039] Example 3, see Figure 1 This embodiment is based on the above embodiment. The translation confidence interval construction module constructs the translation confidence interval, which characterizes the degree of deviation between the translation result and the standard translation. The smaller the interval, the more reliable the translation. A negative interval indicates sufficient confidence and no significant deviation. A positive and larger interval indicates a larger translation deviation and is close to error / noise. The translation confidence interval is expressed as follows: ; It is a translation discrimination model Translation quality confidence score; It is the translation confidence interval. This indicates the existence of prediction bias, and the larger the value, the more severe the bias. This indicates that the prediction has sufficient confidence and no significant bias.

[0040] Example 4, see Figure 1 This embodiment is based on the above embodiment, and the translation quality loss function construction module specifically includes:

[0041] The basic smoothing framework design limits the smoothing of arbitrarily increasing losses, suppressing the influence of noise. Represented as: ; It is a robust smoothing coefficient, with a value ranging from 0.1 to 10; It is a non-negative weighted function. It is an arbitrary, monotonically and continuously increasing basic loss term used to characterize the degree of translation bias;

[0042] The translation quality loss function is designed by assigning independent constraint strengths to low-frequency specialized corpora and high-frequency general corpora, thereby strengthening the constraint on low-frequency mistranslations. Represented as: ; ;in, and These are the constraint coefficients for low-frequency professional samples and high-frequency general samples, respectively. Fixed at 1, Weak constraints are applied to high-frequency samples, with values ​​ranging from 1 to 10; a is a positive even-numbered hyperparameter that controls the loss curvature, with values ​​{2, 4, 6}. It is the translation bias loss term;

[0043] Theoretical constraints are constructed, the upper limit of the loss is fixed, and extreme noise / abnormal translations will not dominate the training, as follows: Confidence level discrimination neighborhood smoothness constraint and far-field convergence suppression.

[0044] Example 5, see Figure 1 This embodiment, based on the above embodiment, directly maps sentence category (low frequency / high frequency) to constraint coefficients, achieving the following: Category-based hierarchical constraints are applied to all samples (regardless of their proximity to the decision boundary), with significantly higher constraint weights for low-frequency professional translation errors compared to general translation errors; the category-constraint association is represented as: b>1 is the balance coefficient; low-frequency professional samples High-frequency general samples ; It is the translation category constraint coefficient.

[0045] By performing the above operations, this solution addresses the problems of general English translation management systems, such as inadequate adaptation to sentence structure differences, poor adaptability to professional scenarios, and the reduction of overall discrimination ability due to extreme noise, which fails to highlight the severity of low-frequency professional translation errors, thus leading to poor reliability in translation management. This solution quantifies the degree of deviation by defining translation confidence intervals, supporting differentiated processing of minor deviations and serious errors; it implements smoothing and grading constraints on sentence structure by designing a translation quality loss function, limiting the dominance of extreme noise; and it ensures that all samples are subject to grading constraints through perceptual constraint coefficient mapping, with higher constraint weights for professional samples, thereby improving the reliability of English translation management.

[0046] Example 6, see Figure 1 This embodiment, based on the above embodiments, embeds the translation quality loss function into the translation quality control framework in the translation result discrimination model construction module, simultaneously achieving the following objectives: preventing overfitting to low-quality corpora, strong constraints on low-frequency specialized translations, weak constraints on high-frequency general translations, and controllable loss limits for noisy translation samples; specifically including:

[0047] The model architecture design comprises a four-layer end-to-end architecture: a feature layer, a discriminant layer, a loss layer, and a constraint optimization layer. Based on a corpus, it receives source language (Chinese) features and target language (English) features as joint input. The feature layer fuses and encodes the bilingual features, extracting source-target semantic matching features, weakening noise, and strengthening key semantics and alignment relationships. The discriminant layer outputs in parallel: translation quality confidence score and sentence category prediction, sharing features and using independent classification heads. The loss layer takes the confidence interval as input and incorporates the translation quality loss function and the sentence category prediction loss term. The weighted sum is used as the total loss of the model to achieve hierarchical constraints on high and low frequency samples; the constraint optimization layer minimizes the objective function and updates the core parameters through the volatility suppression gradient algorithm. It is the continuous confidence score of the sentence type output by the model; When the classification is correct and the margin is large enough, the loss is 0, and the model does not require additional constraints. At that time, classification errors or insufficient margins result in linearly increasing losses, driving model optimization;

[0048] Construct an objective function to directly manage translation features and confidence levels end-to-end in the original feature space. Further nonlinear embedding is then used to capture complex semantics, syntax, and contextual relationships. By mapping the input to a high-dimensional feature space through a kernel function, asymmetric, constrained, and imbalanced perception is achieved, supporting translation quality control and noise filtering under complex semantics. The final optimization objective of the model is expressed as: ;in, It is a translation quality judgment weight vector, which is initialized with zero and has low noise. It is a high-dimensional nonlinear eigenmap using a Gaussian kernel function; is the loss weight, with a value ranging from 0.1 to 1.0; C is the global regularization coefficient, with a value of 10. -3 ~10 3 To balance model complexity and translation accuracy, a grid search interval is used. It is a translation quality label.

[0049] Example 7, see Figure 1 This embodiment is based on the above embodiment. The translation result discrimination model training module adopts adaptive weighted volatility suppression gradient optimization to reduce gradient estimation volatility, improve the convergence stability of low-frequency professional translation, and introduce domain type weights to ensure that the gradient contribution of low-frequency professional samples is not submerged while suppressing gradient volatility. The specific operation is as follows: set the gradient update rule, expressed as: ; ; ;in, and The translation results at step t and step (t-1) are used to determine the model parameters. and These are the learning rates for round s+1 and round s, respectively. It is the attenuation coefficient, with a value of 10. -4 ~10 -2 ; It is a periodic global reference parameter; It is the gradient of the objective function across the entire corpus; It is a single-sample stochastic gradient; the initial learning rate is 10. -4 ~10 -1 ; It is the domain type weight; These are the gradient weights for low-frequency professional samples, with values ​​ranging from 1.5 to 3.0; These are the gradient weights for high-frequency general samples, with values ​​ranging from 1.0 to 1.5; ;

[0050] Regarding the model training process:

[0051] (1) Initialization: Set hyperparameters and initialize the parameters of the translation result discrimination model. and global reference parameters ;

[0052] (2) Iterative training: In the s-th round, calculate the gradient of the objective function across the entire corpus. Step t: Randomly select sample i t Calculate the gradient and Update parameters Repeat the inner loop, updating after each round. and learning rate;

[0053] (3) Termination occurs when the parameter change is less than the threshold (value 10). -4 ~10 -5 The process can be stopped when the maximum number of rounds (ranging from 100 to 500) is reached, thus obtaining the optimal parameters.

[0054] Example 8, see Figure 1 This embodiment is based on the above embodiment. The English translation management module embeds the trained model into the translation management process and calculates the real-time translation quality confidence score. , is the final output (continuous confidence, 0~1) of the translation result discrimination model; the category-adaptive translation decision is represented as: ;in, This is the translation quality threshold, ranging from 0.5 to 0.9; It is a judgment of translation quality. This indicates that the translation quality is acceptable and can be used directly. This indicates that the translation quality is low; X and T represent the source language to be translated and the corresponding target language, respectively.

[0055] Complete translation management process: The user inputs the Chinese sentence to be translated, an independent translation engine is invoked to generate an English translation, which is then converted into Chinese source language feature vectors and English translation feature vectors respectively; the bilingual features are input into the translation result discrimination model to calculate the real-time translation quality score and obtain the translation quality judgment result of the Chinese sentence. If the translation quality is low, it is re-translated. If the translation quality is low three times in a row, it is judged as noise and removed.

[0056] By performing the above operations, this solution addresses the problems of general English translation management systems being dragged down by a large number of low-quality translation samples, biased judgment boundaries due to noise, and inaccurate evaluation of real translations, as well as insufficient attention to professional low-frequency translations, leading to poor translation management results. This solution introduces a translation quality loss function and sentence type prediction loss, designs an objective function to achieve asymmetric, constrained, and unbalanced perception, and is more adaptable to complex semantics. Based on adaptive weighted volatility suppression gradient optimization, it ensures that professional low-frequency translation errors receive a larger gradient response and stronger penalty, meeting the management needs of professional English translation scenarios where terminology mistranslation has a significant impact, improving the judgment ability of low-frequency professional translations, and thus improving the overall effectiveness of English translation management.

[0057] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention.

[0058] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. An English translation management system based on artificial intelligence, characterized by: The system includes a translation corpus acquisition module, a translation confidence interval construction module, a translation quality loss function construction module, a perceptual constraint coefficient mapping module, a translation result discrimination model construction module, a translation result discrimination model training module, and an English translation management module; The translation corpus acquisition module obtains bilingual parallel corpora, labels them with translation quality and sentence type tags, and constructs a corpus set. The translation confidence interval construction module constructs translation confidence intervals that characterize the degree of translation deviation; The translation quality loss function construction module is designed to differentiate between low-frequency specialized corpora and high-frequency general corpora. The perception constraint coefficient mapping module maps sentence categories to category-level constraints based on sentence category labels and balance coefficients. The translation result discrimination model construction module is based on the corpus set, designs a four-layer end-to-end architecture, embeds the translation quality loss function, and constructs the translation result discrimination model. The translation result discrimination model training module performs gradient optimization on the translation result discrimination model to complete the model training; The English translation management module implements English translation management based on a trained translation result discrimination model.

2. The AI-based English translation management system according to claim 1, characterized in that: The translation confidence interval construction module constructs a translation confidence interval, which characterizes the degree of deviation between the translation result and the standard translation.

3. The AI-based English translation management system according to claim 2, characterized in that: The translation quality loss function construction module specifically includes: The basic smoothing framework design limits the smoothing of losses that grow arbitrarily. The translation quality loss function is designed to assign independent constraint strengths to low-frequency specialized corpora and high-frequency general corpora, thereby strengthening the constraint on low-frequency mistranslations. Theoretical constraints are established, and the upper limit of loss is fixed.

4. The AI-based English translation management system according to claim 3, characterized in that: The perceptual constraint coefficient mapping module directly maps sentence categories to constraint coefficients, thereby applying class-based constraints to all samples.

5. The AI-based English translation management system according to claim 4, characterized in that: The translation result discrimination model construction module specifically includes: The model architecture is designed as a four-layer end-to-end architecture for translation result discrimination, consisting of a feature layer, a discrimination layer, a loss layer, and a constraint optimization layer. Based on a corpus, it receives source language features and translation features as joint input. The feature layer fuses and encodes the bilingual features to extract source-translation matching semantic features. The discrimination layer outputs in parallel: translation quality confidence and sentence category prediction. The loss layer takes the confidence interval as input and uses the weighted sum of the translation quality loss function and the sentence category prediction loss term as the model's total loss. The constraint optimization layer minimizes the objective function and updates the parameters using a volatility-suppressing gradient algorithm. Construct an objective function to directly manage translation features and confidence levels end-to-end in the original feature space, thereby obtaining the final optimization objective of the model.

6. The AI-based English translation management system according to claim 5, characterized in that: The translation result discrimination model training module adopts adaptive weighted volatility suppression gradient optimization of the translation result discrimination model and introduces domain type weights; thus completing the model training.

7. The AI-based English translation management system according to claim 6, characterized in that: The English translation management module embeds the trained model into the translation management process, calculates the translation quality confidence score, and makes category-adaptive translation decisions.

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