A system for context-dependent language translation

Abstract

A system (100) for context-sensitive language translation, comprising: an input interface module (102) configured to allow the user to input text for language translation, wherein the input interface module (102) includes an interface that allows the user to input text and select the language for translation; a context processing module (104) connected to the input interface module (102) and configured to enable context-dependent extraction of information from the provided input text, wherein the context processing module (104) comprises the following: an input processing module (106) configured to decompose the input text into tokens and identify context-sensitive segments; and a context extraction module (108) connected to the input processing module (106), wherein the context extraction unit (108) comprises an extraction processor configured to generate context vectors from source input text; a translation processing unit (110) comprising a machine learning-based processor configured to translate the provided input text, wherein the translation processing unit comprises: an attention mechanism (112) configured to select words based on their relevance to the context; and a translation generation module (114) configured to generate translations using a neural network that incorporates the attention-weighted context vectors; a post-processing unit (116) connected to the translation processing unit (110) and configured to refine the translated output to ensure fluency and coherence, refining the translation grammar, ensuring semantic consistency, validating contextual appropriateness, and generating a fluency score; and an output module (118) connected to the post-processing unit (116) and comprising a display with an output interface on which the input text and the translated output text are displayed to the user.

Description

FIELD OF THE INVENTIONThe present disclosure relates to a contextual language translation system. More particularly, the present invention relates to a system that uses advanced natural language processing (NLP) and machine learning techniques to dynamically include contextual information from the surrounding text, thereby enabling more accurate and more liquid translations.BACKGROUND OF THE INVENTIONSpeech translation has evolved significantly by neural machine translation models (NMT) and improved syntactic and semantic accuracy. However, traditional NMT models operate sentence by sentence and often do not cover the broader contextual nuances that are essential for accurate translation.Current translation systems have problems with polysem words and idiopathic expressions, resulting in translations that may be technically correct but contextually inappropriate. This limitation results in three major problems: loss of contextual meaning, inaccurate translations taking into account whole text, and lack of natural fluid in the output.While existing models are excellent in detecting relationships within individual phrases, they typically ignore the context of the surrounding text, resulting in translations that do not maintain coherence across larger documents. Previous attempts to solve these problems, as seen in traditional NMT approaches, focused primarily on improving sentence-level accuracy without sufficiently accounting for the broader context in which these sentences appear.This invention overcomes these limitations by introducing a system that dynamically incorporates contextual information into the translation process, thus enabling more accurate and contextual translations.SUMMARY OF THE INVENTIONThe present disclosure relates to a contextual language translation system. The proposed system utilizes advanced natural language processing (NLP) and machine learning techniques to dynamically include contextual information from the surrounding text to enable more accurate and more liquid translations. Unlike existing models that focus primarily on sentence level translation, this approach takes into account the broader context, including previous and subsequent phrases, to generate translations that are reasonable and semantically coherent depending on the context. The proposed system is configured to extract the context vectors from the source text, which are then integrated into the translation via a sophisticated attention mechanism that assigns weights to words based on their contextual relevance. This dynamic adaptation mechanism ensures that the translation model continues to respond to the nuances of the source text, thus creating translations that better reflect the intended meaning. Extensive testing and evaluations from Benchmark data sets show superior performance of the system and show marked improvements in translation accuracy and naturalness. Comparative analysis with conventional translation highlights the advantages of this contextual approach, especially when dealing with idiopathic expressions, polysemia and syntactic variations. The results emphasize the potential of this system to set a new standard in the field of machine translation.The present disclosure is intended to provide a system for context sensitive language translation. The system comprises: an input interface module configured to allow the user to input text for language translation, the input interface module comprising an interface that allows the user to insert text and select the language for translation; a context processing module connected to the input interface module and configured to allow context-sensitive extraction of information from the provided input text; a translation processing unit comprising a machine learning-based processor configured to translate the provided input text; a post-processing unit connected to the translation processing unit and configured to refine the translated output to ensure liquid and coherence, wherein the refined translation grammar refines, semantic consistency ensures contextual suitability, and a liquid score is generated; and an output module connected to the post-processing unit and comprising a display that displays an output interface that displays the input text and the translated output text to the user.An object of the present disclosure is to provide a contextual language translation system.Another object of the present disclosure is to improve the accuracy of translations by using context vectors that capture the semantic and syntactic relationships within the more comprehensive text.Another object of the present disclosure is to ensure that translations are contextually appropriate and the intended meaning is maintained in different contexts and usage scenarios.Another object of the present disclosure is to provide an accurate, liquid, and natural translation that closely approximates the human translation quality.In order to further clarify the advantages and features of the present disclosure, a more detailed description of the invention will be given with reference to specific embodiments thereof illustrated in the accompanying drawings. It should be noted that this drawing illustrates only typical embodiments of the invention and is therefore not to be considered as limiting its scope. The invention will be described and explained with additional details and details with reference to the accompanying drawings.BRIEF DESCRIPTION OF THE FIGUREThese and other features, aspects, and advantages of the present disclosure will become more fully understood when the following detailed description is read with reference to the accompanying drawings, in which like characters represent like parts throughout. The following applies here: FIG. 1 shows a block diagram of a context-sensitive language translation system according to an embodiment of the present disclosure.Moreover, those skilled in the art will appreciate that elements are shown in the drawing for simplicity and need not necessarily be drawn to scale. For example, the flowcharts illustrate the method using the major steps to improve understanding of aspects of the present disclosure. Moreover, with respect to the construction of the apparatus, one or more components of the apparatus may have been represented in the drawing by conventional symbols, and the drawing may only show the specific details relevant to understanding the embodiments of the present disclosure so as not to obscure the drawing with details readily apparent to those skilled in the art having the benefit of the description herein.DETAILED DESCRIPTION:In order to aid in the understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings and described in specific language. It is to be understood, however, that no limitation of the scope of the invention is intended, since such changes and further modifications to the illustrated system and such further applications of the principles of the invention as illustrated therein are contemplated, as would normally occur to one skilled in the art to which the invention pertains.It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be limiting thereof.References throughout this specification to "one aspect," "another aspect," or similar language mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrase "in one embodiment," "in another embodiment," and similar language in this specification may or may not all refer to the same embodiment.The terms "comprises," "comprising," or other variations thereof are intended to cover a non-exclusive inclusion, such that a process or method comprising a list of steps not only comprises those steps, but may also comprise other steps not expressly listed or inherent in such a process or method. Likewise, one or more devices or subsystems or elements or structures or components preceded by "comprises...a" do not exclude, without further limitations, the existence of other devices or other subsystems or other elements or other structures or other components or additional devices or additional subsystems or additional elements or additional structures or additional components.Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The system, methods, and examples provided herein are illustrative only and are not to be considered limiting.Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.The functional units described in this specification have been referred to as devices. A device may be implemented in programmable hardware devices such as processors, digital signal processors, central processing units, field programmable gate arrays, programmable array logic, programmable logic devices, cloud processing systems, or the like. The devices may also be implemented in software for execution by various types of processors. An identified device may include executable code and may include, for example, one or more physical or logical blocks of computer instructions, which may be organized, for example, as an object, procedure, function, or other construction. Nevertheless, the executable of an identified device need not be physically arranged together, but may include various instructions stored in various locations that, when logically interconnected, form the device and fulfil the stated purpose of the device.Indeed, executable code of a device or module may be a single instruction or multiple instructions, and may even be distributed across multiple different code segments, among different applications, and across multiple storage devices. Similarly, operational data may be identified and illustrated herein within the device and embodied in any suitable form and organized in any suitable type of data structure. The operational data may be acquired as a single dataset or distributed across different locations including different storage devices and may be present at least partially as electronic signals in a system or network.References throughout this specification to "a selected embodiment," "an embodiment," or "an embodiment" mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the disclosed subject matter. Therefore, the appearances of the phrases "a selected embodiment," "in one embodiment," or "in one embodiment" in various places in this specification are not necessarily referring to the same embodiment.Moreover, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to provide a thorough understanding of the embodiments of the disclosed subject matter. However, one skilled in the art will recognize that the disclosed subject matter may be practiced without one or more of the specific details or with other methods, components, materials, etc. In other instances, well-known structures, materials, or operations are not shown or described in detail in order to avoid obscuring aspects of the disclosed subject matter.According to the example embodiments, the disclosed computer programs or modules may be executed in many example ways, such as an application stored in a device's memory or a hosted application executing on a server and communicating with the device application or browser via a number of standard protocols such as TCP / IP, HTTP, XML, SOAP, REST, JSON, and other suitable protocols. The disclosed computer programs may be written in example programming languages that execute from memory on the device or from a hosted server, such as BASIC, COBOL, C, C++, Java, Pascal, or scripting languages such as JavaScript, Python, Ruby, PHP, Perl, or other suitable programming languages.Some of the disclosed embodiments include or otherwise include data transfer over a network, such as communication of various inputs or files over the network. The network may include, for example, one or more of the following: Internet, Wide Area Networks (WANs), Local Area Networks (LANs), analog or digital wired and wireless telephone networks (e.g., a PSTN, Integrated Services Digital Network (ISDN), a cellular network and digital subscriber line (xDS)), radio, television, cable, satellite, and / or any other transmission or tunneling mechanism for transmitting data. The network may include multiple networks or sub-networks, each of which may include, for example, a wired or wireless data path. The network may comprise a circuit switched voice network, a packet switched data network or any other network capable of transmitting electronic communication. For example, the network may comprise networks based on Internet Protocol (IP) or Asynchronous Transfer Mode (ATM), and may support voice using, for example, VoIP, voice over ATM, or other comparable protocols used for voice data communication. In one implementation, the network comprises a mobile telephone network configured to exchange text or SMS messages.Examples of the network include, but are not limited to, a personal area network (PAN), storage area network (SAN), home area network (HAN), campus area network (CAN), local area network (LAN), wide area network (WAN), metropolitan area network (MAN), virtual private network (VPN), enterprise private network (EPN), the Internet, global area network (GAN), and so forth.FIG. 1 shows a block diagram of a context-sensitive language translation system (100) according to an embodiment of the present disclosure.Referring to FIG. 1, the system (100) includes an input interface module (102) configured to allow the user to input text for translation of speech, wherein the input interface module (102) includes an interface that allows the user to insert text and select the speech for translation; a context processing module (104) coupled to the input interface module (102) and configured to facilitate context sensitive extraction of information from the provided input text, the context processing module (104) comprising an input processing module (106) configured to break the input text into tokens and identify context sensitive segments, and a context extraction module (108) coupled to the input processing module (106), wherein the context extraction unit (108) comprises an extraction processor configured to generate context vectors from the source input text; a translation processing unit (110) comprising a machine learning-based processor configured to translate the provided input text, the translation processing unit comprising an attention mechanism (112) configured to weight words based on their relevance to the context, and a translation generation module (114) configured to generate translations using a neural network involving the attention-weighted context vectors; a post-processing unit (116) connected to the translation processing unit (110) and configured to refine the translated output to ensure liquid and coherence, wherein the translation grammar refines, ensures semantic consistency, validates contextual suitability, and generates a liquid value; and an output module (118) connected to the post-processing unit (116) and comprising a display that displays an output interface that displays the input text and the translated output text to the user.In one embodiment, the system (100) further comprises a storage unit (120) configured to store input data, context vectors, and translation data, the storage unit being connected to all components, the storage unit further configured to store pre-trained speech models, manage context vector databases, cache frequent translations, and archive translation patterns.In one embodiment, the context processing module (104) is further configured to perform tokenization of the input text, perform sliding window analysis of the surrounding text, generate semantic relationship vectors, and create syntactic relationship embeds.In one embodiment, the translation processing unit (110) further comprises a feedback module (122) configured to collect translation quality measurements, analyze contextual accuracy, update translation patterns, and optimize model parameters.In one embodiment, attention mechanism (112) includes: a vector integration processor (112a) configured to implement attention mechanisms; and a weight calculation processor (112b) configured to assign contextual relevance weights.In one embodiment, the translation generation module (114) comprises: a neural network engine (114a) configured to process weighted context vectors; and a dynamic adaptation module (114b) configured to contextually change translations.In one embodiment, the vector integration processor (112a) is further configured to calculate dot product attention values, implement scaled dot product attention mechanisms, integrate context vectors with token embeddings, and manage attention weight matrices, and wherein the weight calculation processor (112b) is further configured to calculate context relevance values, generate weight distributions across context vectors, normalize attention weights, and update weights based on semantic relationships.In one embodiment, the neural network engine (114a) is further configured to process contextual embeds, implement pre-trained speech models, execute translation model inference, and maintain translation accuracy metrics, and wherein the dynamic adaptation module (114b) is further configured to change translations based on contextual embeds, implement translation adaptations in real-time, evaluate contextual coherence, and optimize translation liquid.The present invention relates to a contextual language translation system. The proposed system uses advanced natural language processing (NLP) and machine learning techniques to perform contextual language translations. The core mechanisms that the proposed system uses are context extraction, attention mechanism and dynamic adaptation. The context extraction mechanism extracts relevant context information from the source text and creates context vectors representing the semantic and syntactic relationships within the text. The attention mechanism integrates context vectors into the translation process and assigns weights to words based on their contextual relevance. The dynamic mapping dynamically adjusts translations based on the context vectors and ensures that the generated translations are context relevant and semantically coherent.The system includes three major components or mechanisms, context extraction, context vector integration, and dynamic adaptation. The proposed system is configured to first process the input text, tokenizing the input text and identifying context sensitive segments. A context extraction is then carried out, in which the context vectors are derived from the surrounding text. The system is configured to implement an attention mechanism that weights the words based on their relevance to the context. Based on the above mechanisms, the translation is generated using a neural network that involves the attention-weighted context vectors. The translated output is post-processed for refinement to ensure liquid and coherence. The operation of the main mechanisms of the system will be described in detail below.The most important mechanism of the proposed system is context extraction. This mechanism involves identifying and acquiring relevant context information from the source text that serves as the basis for the translation process. Context extraction is performed by tokenization, in which the input text is broken down into individual tokens such as words or subwords. Next, a sliding window approach is applied. This technique captures the surrounding text within a particular window size, for example two phrases before and after the target phrase. By including adjacent sets, the system ensures that sufficient context information is available to improve translation accuracy and coherence. Finally, the creation of context vectors is performed, in which context vectors are generated in order to encapsulate the semantic and syntactic relationships of the words within the window. These vectors are created using pre-trained speech models such as BERT, GPT, or other context embedding techniques. These models provide deep, nuanced representations of the text so that the system can better understand and use contextual nuances.The system is also configured to implement an attention mechanism that dynamically determines the importance of context vectors during the translation process. This allows the system to focus on relevant portions of the input sequence for each output token. The mechanism calculates the attention values for each token based on its context vectors. These values are determined using mechanisms such as dot product attention or scaled dot product attention that measure the relevance of each token in the input sequence with respect to the current translation step. Once the attention values are calculated, the system assigns weights to each context vector. These weights are proportional to the attention values so that the system can emphasize and play down more relevant context vectors. Finally, the system integrates the weighted context vectors into the translation process. This integration ensures that the model uses the most relevant information from the input sequence to generate accurate and contextually appropriate translations.The system is configured to perform dynamic matching in which the translation process is changed based on integrated context vectors to ensure contextually appropriate translations. First, contextual embedding is performed, in which the system combines the token embedding with the weighted context vector to create a contextual embedding. This embedding integrates relevant context information into the token representation, so that the system can better understand the nuances of the source text. The translation generation is performed by the system using contextual embeddings and occurs via a neural machine translation model (NMT). The inclusion of contextual embeddings ensures that the translations better conform to the intended meaning of the source text. The system is configured to perform post-processing of the generated translation, where the generated translations are refined to ensure liquid and coherence. This refinement helps to produce translations which are not only accurate but also natural and easily understood.The proposed system is simulated and its performance evaluated. The system is tested using Benchmark (WMT) data sets containing parallel multiple language korspora)) and its performance is compared to existing translation systems. The system is also fine tuned based on the evaluation results to optimize accuracy and liquid. The scoring scales include the BLEU score for measuring accuracy, the human score for assessing the naturalness and fluid of translation, and contextual coherence for assessing the ability to maintain contextual relevance across phrases.The results show how effectively the proposed context sensitive translation system improves translation quality. The system achieved a significantly higher BLEU score of 35.2 compared to 29.5 for the transformer base model, reflecting improved translation accuracy. Human evaluations additionally confirmed the performance of the system, with the average evaluations for fluid and naturalness at 4.4 and 4.3, respectively, exceeding the values of the baseline model of 3.8 and 3.6, respectively. Moreover, with respect to contextual coherence, the system obtained a score of 4.5, indicating a better ability to maintain the intended meaning across sets compared to the score of the base model of 3.2.The system has significant improvements in translation accuracy, translation liquid, and contextual coherence by dynamically incorporating contextual information into the translation process. This ability is critical to address challenges such as dealing with polysemous words, idiopathic expressions and culture specific references. By utilizing weighted context vectors and an attention mechanism, the system ensures contextually reasonable and semantically coherent translations.Despite its advantages, the system presents challenges such as increased computational complexity due to context extraction and attention mechanisms. Moreover, the dependence of pre-trained models for context extraction shows that this depends on the quality of these models. These constraints suggest the need for optimization to improve efficiency and scalability.Future advancements of the system may include optimizing the context window size to compensate for context information and computational effort, adapting the system to particular ranges such as legal or medical translation, and developing real-time translation functions.The invention provides a context sensitive translation system that integrates context information into the translation process and overcomes critical limitations of conventional neural machine translation models. The system achieves significant improvements in translation accuracy, translation liquid, and context coherence as evidenced by higher BLEU values and better human scores. The innovative approach to considering contextual nuances positions this system as a major advance in machine translation technology with the potential for broad applicability in different languages and areas. The invention sets forth a new measure of translation quality and simultaneously levels the way for future improvements in efficiency and adaptability.The drawings and the foregoing description provide examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be divided into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of the processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow chart need not be implemented in the order shown; nor do all actions necessarily need to be performed. Also, those actions that are not dependent on other actions may be performed in parallel with the other actions. The scope of the embodiments is in no way limited by these specific examples. Numerous variations, whether or not explicitly stated in the specification, such as differences in structure, dimension, and material usage, are possible. The scope of the embodiments is at least as broad as recited in the following claims.Advantages, other advantages, and solutions to problems have been described above with respect to certain embodiments. However, the advantages, benefits, solutions to problems, and any components that may result in an advantage, solution occurring or becoming clearer are not to be construed as a critical, required, or essential feature or component of any or all claims.REFERENCES100 A Contextual Language Translation System. 102 Input interface module 104 Context processing module 106 Input processing module 108 Context extraction module 110 Translation processing unit 112 Attention mechanism 112 aVector integration processor 112 bWeight calculation processor 114 Module for generating translations 114 a Neuronale network engine 114 bDynamic adaptation module 116 Post processing unit 118 Output module 120 Storage unit 122 Feedback module

Claims

A context-sensitive language translation system (100) comprising: an input interface module (102) configured to allow the user to input text for language translation, the input interface module (102) comprising an interface that allows the user to input text and select the language for translation; a context processing module (104) coupled to the input interface module (102) and configured to allow contextual extraction of information from the provided input text, the context processing module (104) comprising: an input processing module (106) configured to break the input text into tokens and identify context-sensitive segments; and a context extraction module (108) coupled to the input processing module (106), the context extraction unit (108) comprising an extraction processor configured to generate context vectors from source input text; a translation processing unit (110) comprising a machine learning-based processor configured to translate the provided input text, the translation processing unit comprising: an attention mechanism (112) configured to generate words based on their relevance to the context; and a translation generation module (114) configured to generate translations using a neural network involving the attention-weighted context vectors; a post-processing unit (116) connected to the translation processing unit (110) and configured to refine the translated output to ensure liquid and coherence, wherein the translation grammar refines, semantic consistency ensures contextual suitability, validates contextual suitability, and generates a liquid value; and an output module (118) connected to the post-processing unit (116) and comprising a display having an output interface on which the input text and the translated output text are displayed to the user.The system of claim 1, further comprising a storage unit (120) configured to store input data, context vectors, and translation data, wherein the storage unit is coupled to all components, wherein the storage unit is further configured to store pre-trained language models, manage context vector databases, cache frequent translations, and archive translation patterns.The system of claim 1, wherein the context processing module (104) is further configured to perform tokenization of the input text, perform sliding window analysis of the surrounding text, generate semantic relationship vectors, and create syntactic relationship embeds.The system of claim 1, wherein the translation processing unit (110) further comprises a feedback module (122) configured to collect translation quality measurements, analyze contextual accuracy, update translation patterns, and optimize model parameters.The system of claim 1, wherein the attention mechanism (112) comprises: a vector integration processor (112a) configured to implement attention mechanisms; and a weight calculation processor (112b) configured to assign contextual relevance weights.The system of claim 1, wherein the translation generation module (114) comprises: a neural network engine (114a) configured to process weighted context vectors; and a dynamic adaptation module (114b) configured to change translations based on the context.The system of claim 4, wherein the vector integration processor (112a) is further configured to calculate dot product attention values, implement scaled dot product attention mechanisms, integrate context vectors with token embeddings, and maintain attention weight matrices, and wherein the weight calculation processor (112b) is further configured to calculate context relevance values, generate weight distributions across context vectors, normalize attention weights, and update weights based on semantic relationships.The system of claim 5, wherein the neural network engine (114a) is further configured to process contextual embeds, implement pre-trained speech models, execute translation model inference, and maintain translation accuracy metrics, and wherein the dynamic adaptation module (114b) is further configured to change translations based on contextual embeds, implement translation adaptations in real-time, evaluate contextual coherence, and optimize translation liquid.

Images