CN116702801A - Translation method, device, equipment and storage medium - Google Patents
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Abstract
The application discloses a translation method, a device, equipment and a storage medium, which relate to the technical field of language translation, and the method comprises the following steps: identifying the minimum record language to which the acquired data to be translated belongs; screening a target language evolution rule of a region corresponding to the minimum recorded language from a preset language evolution rule library; determining a target language type of the data to be translated directly based on the target language evolution rule, wherein the target language type belongs to a subdivision class in the minimum record language; and translating the data to be translated based on the target language type. In the application, the lowest-level record type of the data to be translated is identified from the recorded language types, and the target language type of the minimum area of the data to be translated is determined according to the language evolution rule, so that the identification error of the language type is reduced, the meaning expressed by the data to be translated is accurately identified, the data to be translated is further accurately translated, and the translation accuracy is improved.
Description
Technical Field
The present application relates to the field of language translation technologies, and in particular, to a translation method, apparatus, device, and storage medium.
Background
In the era of globalization and increasing frequency of cross-cultural communication, translation has become a very important skill, and not only cross-country communication but also cross-language communication can be achieved through translation.
Since each person has a different degree of knowledge of the language, when encountering a language that is not understood by himself, it is preferable to use a translation device or a translation piece for translation. However, when a language is translated by using a translation device or translation software, the language can be translated only according to the type of the language which is already divided, and the language which is not already divided cannot be accurately identified, so that the accuracy of the translation is reduced.
Disclosure of Invention
The application mainly aims to provide a translation method, a translation device, translation equipment and a translation storage medium, and aims to solve the technical problem that in the prior art, translation can only be carried out according to divided language types, and the accuracy of translation is reduced because the language which is not divided cannot be accurately identified.
To achieve the above object, the present application provides a translation method, including:
identifying the minimum record language to which the acquired data to be translated belongs;
screening a target language evolution rule of a region corresponding to the minimum recorded language from a preset language evolution rule library;
determining a target language type of the data to be translated directly based on the target language evolution rule, wherein the target language type belongs to a subdivision class in the minimum record language;
and translating the data to be translated based on the target language type.
Optionally, the step of determining the target language class to which the data to be translated directly belongs based on the target language evolution rule includes:
simulating the evolution process of the language in the minimum record language based on the target language evolution rule, and determining the generic language of each region according to the simulated evolution process;
determining the similarity between the data to be translated and each of the directly-affiliated languages;
and determining the target language type of the data to be translated based on the similarity.
Optionally, the step of determining the similarity between the data to be translated and each of the generic languages includes:
inputting the data to be translated into a preset language feature recognition model to obtain language features of the data to be translated, wherein the language features comprise at least one of tone, accent, grammar, mood and special vocabulary, and the preset language feature recognition model is obtained after model training is carried out through a language sample marked with the language features;
and comparing the language features with the language features of the directly-affiliated languages, and determining the similarity between the data to be translated and each directly-affiliated language.
Optionally, the step of determining the target language class of the data to be translated based on the similarity includes:
screening target languages from the generic languages based on the similarity;
determining the minimum area to which the target language belongs;
and determining the language type of the minimum region as the target language type of the data to be translated.
Optionally, before the step of screening the target language evolution rule of the region corresponding to the minimum record language from the preset language evolution rule library, the method further includes:
acquiring the development history of the region corresponding to each minimum record language;
inquiring regional transition information and language change information of each region from the development history;
determining a language evolution rule of the region based on the region transition information and the language change information;
and integrating all the language evolution rules to obtain a preset language evolution rule.
Optionally, the step of determining the language evolution rule of the region based on the region transition information and the language change information includes:
screening population flow information after each transition of the region from the region transition information;
determining, based on the population flow information, a foreign language flowing into the region and a local language of the region;
analyzing the fusion rule of the foreign language into the local language from the language change information;
and determining the language evolution rule of the region based on the integration rule.
Optionally, the step of translating the data to be translated based on the target language category includes:
identifying the meaning of the data to be translated based on the target language characteristics of the target language types;
and translating the data to be translated based on the meaning.
In addition, in order to achieve the above object, the present application also provides a translation apparatus, including:
the identification module is used for identifying the minimum record language to which the acquired data to be translated belongs;
the screening module is used for screening out a target language evolution rule of a region corresponding to the minimum record language from a preset language evolution rule library;
the first determining module is used for determining the target language type of the data to be translated directly based on the target language evolution rule, wherein the target language type belongs to the subdivision category in the minimum record language;
and the translation module is used for translating the data to be translated based on the target language type.
In addition, to achieve the above object, the present application also proposes a translation apparatus, the apparatus comprising: a memory, a processor, and a translation program stored on the memory and executable on the processor, the translation program configured to implement the steps of the translation method as described above.
In addition, in order to achieve the above object, the present application also proposes a storage medium having stored thereon a translation program which, when executed by a processor, implements the steps of the translation method as described above.
Compared with the prior art that translation can only be carried out according to the classified language types, and the non-classified language cannot be accurately identified, so that the accuracy of the translation is reduced, the method, the device and the storage medium provided by the application identify the minimum record language to which the acquired data to be translated belongs; screening a target language evolution rule of a region corresponding to the minimum recorded language from a preset language evolution rule library; determining a target language type of the data to be translated directly based on the target language evolution rule, wherein the target language type belongs to a subdivision class in the minimum record language; and translating the data to be translated based on the target language type. In the method, the minimum record language of the division of the data to be translated is identified, the language evolution rule of the region corresponding to the minimum record language is combined, the region of the data to be translated is determined, the target language type of the data to be translated is determined, and finally the data to be translated is translated according to the target language type, namely, the minimum record language of the lowest level of the data to be translated is identified from the divided record language types, the target language type of the minimum region of the data to be translated is determined according to the language evolution rule, so that the identification error of the language type is reduced, the meaning expressed by the data to be translated is accurately identified, the data to be translated is accurately translated, and the translation accuracy is improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the description of the embodiments or the prior art will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.
FIG. 1 is a schematic diagram of a device architecture of a hardware operating environment according to an embodiment of the present application;
FIG. 2 is a flow chart of a translation method according to a first embodiment of the present application;
FIG. 3 is a flow chart of a translation method according to a second embodiment of the present application;
fig. 4 is a schematic structural configuration diagram of the translation device of the present application.
The achievement of the objects, functional features and advantages of the present application will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
Referring to fig. 1, fig. 1 is a schematic diagram of a translation device structure of a hardware running environment according to an embodiment of the present application.
As shown in fig. 1, the translation apparatus may include: a processor 1001, such as a central processing unit (Central Processing Unit, CPU), a communication bus 1002, a user interface 1003, a network interface 1004, a memory 1005. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a WIreless interface (e.g., a WIreless-FIdelity (WI-FI) interface). The Memory 1005 may be a high-speed random access Memory (Random Access Memory, RAM) Memory or a stable nonvolatile Memory (NVM), such as a disk Memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
It will be appreciated by those skilled in the art that the structure shown in fig. 1 is not limiting of the translation device and may include more or fewer components than shown, or may combine certain components, or may be arranged in different components.
As shown in fig. 1, an operating system, a data storage module, a network communication module, a user interface module, and a translation program may be included in the memory 1005 as one type of storage medium.
In the translation device shown in fig. 1, the network interface 1004 is mainly used for data communication with other devices; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the translation apparatus of the present application may be provided in the translation apparatus, and the translation apparatus calls the translation program stored in the memory 1005 through the processor 1001 and executes the translation method provided by the embodiment of the present application.
An embodiment of the present application provides a translation method, referring to fig. 2, fig. 2 is a schematic flow chart of a first embodiment of a translation method of the present application.
The present embodiment aims at: and the language type of the lowest level of the data to be translated is accurately determined by utilizing the evolution rule of the target language, and the data to be translated is translated based on the language type, so that the recognition error of the language type is reduced, and the translation accuracy is improved.
In this embodiment, it should be noted that the translation method may be applied to a translation apparatus that belongs to a translation device that belongs to a translation system.
In this embodiment, the translation method includes:
step S10, identifying the minimum record language to which the acquired data to be translated belongs;
the minimum recorded language may be a world language type, a local dialect type, or the like, for example, english, french, japanese, northeast, cantonese, and yunnan dialect, and the like, and is not particularly limited.
It should be noted that the minimum record language may be a class of language classification known to the public and having the lowest level, where the minimum record language may be a class of language classification known to the public such as cantonese, local dialect, southern Min, english, american english or australian english; but also the lowest level of language categories that can be identified by the common language identification model. The target language type may be a language type not known to the public, wherein the target language type may be a dialect of a specific town, a dialect of a specific district, a dialect of a specific city, or a slang of a different region in english, etc. only local people or people in surrounding regions know, a dialect or language not known to the public, that is, a dialect or language not popularized; and the language type of the lowest level which is not recognized by the common language recognition model can be also used.
The data to be translated can be text data or audio data, wherein the audio data can be recording audio or live speaking audio.
Step S20, screening out a target language evolution rule of a region corresponding to the minimum record language from a preset language evolution rule library;
in this embodiment, the preset language evolution rule library includes language evolution rules of multiple language types, where the language evolution rules may include a rule that a language of a high-level region evolves to a language of a middle-low-level region in a region corresponding to the language type. For example, the region corresponding to the minimum record language a has a region B and a region C, the region B is composed of a region B1, a region B2, … …, and a region Bn, the region Bn is composed of a region Bn1, a region Bn2, … …, and a region Bn, and the language evolution rule may be a rule from the language of the region B to the language of the region B1, the region B2, … …, and the region Bn, a rule from the language of the region Bn to the language of the region Bn1, the region Bn2, … …, and the language of the region Bn, or a superposition of the two rules, that is, a rule from the language of the region B to the language of the region Bn1, the region Bn2, … …, and the language of the region Bn, and the like, which is not particularly limited.
In this embodiment, the known lowest-level language types can be continuously subdivided according to the target language evolution rule to obtain the minimum-level languages of different branch regions, the language types with the same or highest similarity with the data to be translated are found out from the languages of the branch regions, and then the meaning of the data to be translated is identified according to the language characteristics of the language types, so that the identification error of the meaning expressed by the data to be translated is reduced, and the accuracy of translation can be improved.
Step S30, determining the target language category of the data to be translated directly based on the target language evolution rule, wherein the target language category belongs to the subdivision category in the minimum record language;
in this embodiment, since the region corresponding to the minimum record language is wide, there may be a plurality of branch languages which are not known to the public, for example, a town language, and the specific semantics of the branch languages are different from the known semantics of the language corresponding to the minimum record language, and the error of the translation result is increased only by translating the data to be translated in the minimum record language. Therefore, after the data to be translated is obtained, the lowest-level class (minimum record language) of the data to be translated among the known language classes is identified, and then the branch language class (target language class) to which the data to be translated directly belongs is determined according to the target language rule.
In this embodiment, since the semantics of different branch languages also have differences, the evolution rule of the target language is used to determine the subdivision direction of the minimum record language, and the branch language type after subdivision, and finally the target language type to which the data to be translated directly belongs is identified from the branch language type, the meaning expressed by the data to be translated can be accurately identified through the target language type, and the data to be translated is translated according to the meaning, so as to improve the accuracy of translation.
For example, if the data to be translated is a foreign language, the identified minimum record language may be american english, australian english, french, japanese, etc., if the identified minimum record language is american english, all regions using american english are first determined, then the language evolution rule of the region, that is, the target language evolution rule, is screened out from the preset language evolution rule library, the branch languages of all sub-regions in the region are determined according to the target evolution rule, the data to be translated is compared with each branch language, the target language with the highest similarity to the data to be translated is found out from the branch languages, the region to which the target language belongs is determined, and the name of the region is used as the target language type name of the data to be translated, for example, W town language.
It should be noted that, since dialects also exist in the foreign language, and the evolution rules of different dialects in the foreign language are also trace-circulated, when the data to be translated is the foreign language, the evolution rules of the target language corresponding to the foreign language can be determined.
Specifically, the step of determining the target language class to which the data to be translated directly belongs based on the target language evolution rule includes:
step S31, based on the target language evolution rule, simulating the evolution process of the language in the minimum record language, and determining the generic language of each region according to the simulated evolution process;
step S32, determining the similarity between the data to be translated and each of the generic languages;
step S33, determining the target language type of the data to be translated based on the similarity.
In this embodiment, according to the target language evolution rule, the language in the minimum recorded language is simulated, the evolution process of the language is used for each county level region or town level region, and the generic language (branch language) of each region is determined according to the evolution process, so that collection of language types can be reduced, and the data volume of a preset corpus can be reduced.
It should be noted that, the general minimum record language has a corresponding corpus, meaning expressed by each vocabulary, sentence, etc. in the minimum record language is stored in the corpus, and the evolution process of the downward branch of the language in the minimum record language can be simulated through the evolution rule of the target language, without collecting a great number of branch language types, thus not only reducing the difficulty of constructing the corpus, but also using the existing corpus to more accurately identify the meaning expressed by the data to be translated.
In this embodiment, since the evolution rule of the target language is obtained by sorting and summarizing the development history of the region corresponding to the minimum record language, there may be an error between the semantics of the generic language and the semantics of the language in the region corresponding to the actual language, that is, the language which is completely the same as the data to be translated may not be found out from the generic language; after the directly affiliated languages are determined, calculating the similarity between the data to be translated and all the directly affiliated languages, finding out the target language corresponding to the data to be translated from the directly affiliated languages according to the similarity, and determining the target language type of the target language so as to ensure the accuracy of identifying the data to be translated.
In this embodiment, the determining the similarity between the data to be translated and the generic language may be determining the similarity between the data to be translated and the generic language. If the data to be translated is text data, the language features can be special vocabulary and/or grammar and the like; if the data to be translated is audio data, the language features may be tones, mood, accents, etc.
Specifically, the step of determining the target language type of the data to be translated based on the similarity includes:
step A10, screening target languages from the generic languages based on the similarity;
step A20, determining the minimum area to which the target language belongs;
and step A30, determining the language type of the minimum region as the target language type of the data to be translated.
In this embodiment, the target language may be selected from the directly-attached languages, where the directly-attached language with the highest similarity to the data to be translated is selected as the target language of the data to be translated; the generic language with similarity to the data to be translated being greater than a preset similarity threshold may be used as the target language of the data to be translated, and the target languages may be multiple.
In this embodiment, a preset similarity threshold is used to determine a target language of data to be translated, so that the fault tolerance of translation can be improved, and because two or more regions corresponding to directly-affiliated languages with high similarity are adjacent, and the languages between adjacent regions are basically communicated, the credibility of the finally determined target language type can be improved through the determined multiple target languages.
It should be noted that, the minimum region may be a region set with the same language features, and since the language features are the same, that is, the meaning expressed by the same sentence in the minimum region is the same, after the target language belongs to the minimum region, the language type of the minimum region can be used as the target language type of the data to be translated, so as to reduce unnecessary recognition process, and further improve the translation efficiency on the premise of improving the translation accuracy.
It should be noted that, the target language type may be the minimum recorded language, and when the minimum recorded language cannot be subdivided continuously according to the evolution rule of the target language, the minimum recorded language may be used as the target language type; or when the language features between the language corresponding to the minimum record language and the language of the direct language are completely the same, the minimum record language can be used as the target language type.
Step S40, translating the data to be translated based on the target language type.
In this embodiment, after identifying the meaning of the data to be translated, it is further required to determine the data type of the translated data, and according to the data type, determine whether to translate the data to be translated into written file data or translate the data to be translated into spoken audio data, so as to meet different requirements of users on the translated data.
In this embodiment, the data to be translated may be a dialect, and the language after translation may be a foreign language, that is, the dialect may be translated into a foreign language, or the foreign language may be translated into the dialect; the method of translating the foreign language into another foreign language is not particularly limited.
Specifically, the step of translating the data to be translated based on the target language type includes:
step S41, based on the target language characteristics of the target language types, identifying the meaning of the data to be translated;
and step S42, translating the data to be translated based on the meaning.
It should be noted that when translating the data to be translated, whether to translate or to use both the translation and the translation can be selected according to the current popular vocabulary, that is, according to the meaning of the data to be translated, the data to be translated is divided into an transliteration word and an meaning translation word, the transliteration word is subjected to transliteration, and the meaning translation word is subjected to meaning translation, so that a user can understand the meaning of the data to be translated more quickly, the translation efficiency is improved, and the translation flexibility is also improved.
In this embodiment, if the target language of the data to be translated is a dialect, the data to be translated may be translated into a language of the smallest record language to which the dialect belongs, and then the language is translated into the target language by using a language evolution rule of the target language, so as to avoid failure in translation caused by no record of the target language in the corpus.
Compared with the prior art that translation can only be carried out according to the classified language types, and the non-classified language cannot be accurately identified, so that the accuracy of the translation is reduced, the method, the device and the storage medium provided by the application identify the minimum record language to which the acquired data to be translated belongs; screening a target language evolution rule of a region corresponding to the minimum recorded language from a preset language evolution rule library; determining a target language type of the data to be translated directly based on the target language evolution rule, wherein the target language type belongs to a subdivision class in the minimum record language; and translating the data to be translated based on the target language type. In the method, the minimum record language of the division of the data to be translated is identified, the language evolution rule of the region corresponding to the minimum record language is combined, the region of the data to be translated is determined, the target language type of the data to be translated is determined, and finally the data to be translated is translated according to the target language type, namely, the minimum record language of the lowest level of the data to be translated is identified from the divided record language types, the target language type of the minimum region of the data to be translated is determined according to the language evolution rule, so that the identification error of the language type is reduced, the meaning expressed by the data to be translated is accurately identified, the data to be translated is accurately translated, and the translation accuracy is improved.
Further, according to the foregoing embodiment of the present application, there is provided another embodiment of the present application, in which, referring to fig. 3, before the step of screening the target language evolution rule of the region corresponding to the minimum record language from the preset language evolution rule library, the method further includes:
step S01, acquiring the development history of the region corresponding to each minimum record language;
step S02, inquiring regional transition information and language change information of each region from the development history;
step S03, determining a language evolution rule of the region based on the region transition information and the language change information;
and S04, integrating all the language evolution rules to obtain a preset language evolution rule.
In this embodiment, the development history may be the development history of all regions corresponding to the minimum recorded language, so as to determine the evolution rule between the language type of each region and the minimum recorded language and the evolution relationship of each region language according to the development history, that is, not only the subdivision rule of the minimum recorded language may be determined according to the development history, but also the evolution rule inside the subdivided branch type may be determined according to the development history, and finally the language evolution rule of each region may be determined according to the subdivision rule and the subdivision rule.
In this embodiment, because the regional transition can enable the language features of other regions to be integrated into the local language features so as to update the local original language features and obtain new language features, the language evolution rule of each region can be determined according to the regional transition information and the language change information of each region in the development history, and the language evolution rule obtained according to the development history is more authoritative.
In this embodiment, since the language evolution rule is determined according to the development history, each word, grammar, accent or change of a specific word, that is, the local language feature evolution rule is included, when a word incapable of being identified according to the language features of the target class exists in the data to be translated, or an uncertain word is identified, the word can be accurately identified according to the language evolution rule, so as to avoid translation failure, or the word is translated in the form of homonyms, so that the target language is more accurately obtained after the data to be translated is translated.
Specifically, the step of determining the language evolution rule of the region based on the region transition information and the language change information includes:
step B10, screening population flow information after each transition of the region from the region transition information;
step B20, based on the population flow information, determining a foreign language flowing into the region and a local language of the region;
step B30, analyzing the fusion rule of the foreign language into the local language from the language change information;
and step B40, determining the language evolution rule of the region based on the integration rule.
In this embodiment, as the population in each region also flows along with the regional transition, the local floating population information can be known according to the regional transition information, the local language can be fused into the external local language along with the population flow, the local language is changed to obtain a new local language, the fusion rule of the local language when the local language flows into the local language can be analyzed according to the language change information, finally, the language evolution rule is determined according to the fusion rule, the language evolution rule can be more in line with the influence of the history on the development of the language in the minimum record language, and the error between the target language obtained by using the language evolution rule and the language in use is reduced, so that the translation error is reduced, and the translation accuracy is improved.
Further, based on the above embodiment of the present application, there is provided another embodiment of the present application, in which the step of determining the similarity between the data to be translated and each of the generic languages includes:
step C10, inputting the data to be translated into a preset language feature recognition model to obtain language features of the data to be translated, wherein the language features comprise at least one of tone, accent, grammar, mood and special vocabulary, and the preset language feature recognition model is obtained after model training is carried out through a language sample marked with the language features;
and step C20, comparing the language features with the language features of the directly-affiliated languages, and determining the similarity between the data to be translated and each directly-affiliated language.
In this embodiment, language features such as tones, accents, grammar, mood, or unique vocabulary of the data to be translated are identified by setting a language feature identification model, so that accuracy of identifying the language features of the data to be translated is improved.
It should be noted that, the language feature recognition model may be continuously learned and updated during the use process, so as to improve the accuracy and recognition efficiency of recognizing the language feature.
In this embodiment, the language features of the data to be translated identified by the language feature identification model are compared with the language features of the directly affiliated languages, so that the error of the similarity between the data to be translated and each of the directly affiliated languages can be reduced.
The present application also provides a translation apparatus, referring to fig. 4, the translation apparatus includes:
the identifying module 401 is configured to identify a minimum record language to which the acquired data to be translated belongs;
the screening module 402 is configured to screen a target language evolution rule corresponding to the region with the smallest record language from a preset language evolution rule library;
a first determining module 403, configured to determine, based on the target language evolution rule, a target language class to which the data to be translated directly belongs, where the target language class belongs to a subdivision class in the minimum record language;
and a translation module 404, configured to translate the data to be translated based on the target language type.
Optionally, the first determining module 403 includes:
the simulation module is used for simulating the evolution process of the language in the minimum record language based on the target language evolution rule, and determining the generic language of each region according to the simulated evolution process;
the first determining submodule is used for determining similarity between the data to be translated and each generic language;
and the second determining submodule is used for determining the target language type of the data to be translated based on the similarity.
Optionally, the first determining submodule includes:
the first acquisition module is used for inputting the data to be translated into a preset language feature recognition model to obtain language features of the data to be translated, wherein the language features comprise at least one of tone, accent, grammar, language gas and special vocabulary, and the preset language feature recognition model is obtained after model training is carried out through language samples marked with the language features;
and the comparison module is used for comparing the language characteristics with the language characteristics of the directly-affiliated languages and determining the similarity between the data to be translated and each directly-affiliated language.
Optionally, the second determining submodule includes:
the first screening submodule is used for screening target languages from the generic languages based on the similarity;
a first determining unit, configured to determine a minimum region to which the target language belongs;
and the second determining unit is used for determining the language type of the minimum area as the target language type of the data to be translated.
Optionally, the translation device further includes:
the second acquisition module is used for acquiring the development history of the region corresponding to each minimum record language;
the inquiry module is used for inquiring the regional transition information and the language change information of each region from the development history;
the second determining module is used for determining the language evolution rule of the region based on the region transition information and the language change information;
and the integration module is used for integrating all the language evolution rules to obtain a preset language evolution rule.
Optionally, the second determining module includes:
the second screening submodule is used for screening population flow information after each transition of the region from the region transition information;
a first determining unit configured to determine, based on the population flow information, a foreign language flowing into the region and a local language of the region;
the analysis module is used for analyzing the fusion rule of the foreign language when the local language is fused into the language from the language change information;
and the second determining unit is used for determining the language evolution rule of the region based on the integration rule.
Optionally, the translation module 404 includes:
the recognition sub-module is used for recognizing the meaning of the data to be translated based on the target language characteristics of the target language types;
and the translation sub-module is used for translating the data to be translated based on the meaning.
The specific implementation manner of the translation device of the present application is substantially the same as that of each embodiment of the translation method described above, and will not be repeated here.
The embodiment of the application provides a storage medium, and the storage medium stores one or more programs, and the one or more programs can also be executed by one or more processors to implement the steps of the translation method of any one of the above.
The specific implementation manner of the storage medium of the present application is basically the same as that of each embodiment of the translation method, and will not be repeated here.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising a" or "comprising an" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The foregoing embodiment numbers of the present application are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method of the embodiments of the present application.
The foregoing description of the preferred embodiments of the present application should not be taken as limiting the scope of the application, but rather should be understood to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the application as defined by the following description and drawings.
Claims (10)
1. A method of translation, the method comprising:
identifying the minimum record language to which the acquired data to be translated belongs;
screening a target language evolution rule of a region corresponding to the minimum recorded language from a preset language evolution rule library;
determining a target language type of the data to be translated directly based on the target language evolution rule, wherein the target language type belongs to a subdivision class in the minimum record language;
and translating the data to be translated based on the target language type.
2. The translation method according to claim 1, wherein said step of determining a target language class to which said data to be translated directly belongs based on said target language evolution law comprises:
simulating the evolution process of the language in the minimum record language based on the target language evolution rule, and determining the generic language of each region according to the simulated evolution process;
determining the similarity between the data to be translated and each of the directly-affiliated languages;
and determining the target language type of the data to be translated based on the similarity.
3. The translation method according to claim 2, wherein said step of determining the similarity of said data to be translated to each of said subordinate languages comprises:
inputting the data to be translated into a preset language feature recognition model to obtain language features of the data to be translated, wherein the language features comprise at least one of tone, accent, grammar, mood and special vocabulary, and the preset language feature recognition model is obtained after model training is carried out through a language sample marked with the language features;
and comparing the language features with the language features of the directly-affiliated languages, and determining the similarity between the data to be translated and each directly-affiliated language.
4. The translation method according to claim 2, wherein said step of determining the target language class of the data to be translated based on the similarity comprises:
screening target languages from the generic languages based on the similarity;
determining the minimum area to which the target language belongs;
and determining the language type of the minimum region as the target language type of the data to be translated.
5. The method of claim 1, wherein before the step of screening the target language evolution rule of the region corresponding to the minimum recorded language from the library of preset language evolution rules, the method further comprises:
acquiring the development history of the region corresponding to each minimum record language;
inquiring regional transition information and language change information of each region from the development history;
determining a language evolution rule of the region based on the region transition information and the language change information;
and integrating all the language evolution rules to obtain a preset language evolution rule.
6. The translation method according to claim 5, wherein said step of determining a language evolution law of said region based on said region transition information and said language change information comprises:
screening population flow information after each transition of the region from the region transition information;
determining, based on the population flow information, a foreign language flowing into the region and a local language of the region;
analyzing the fusion rule of the foreign language into the local language from the language change information;
and determining the language evolution rule of the region based on the integration rule.
7. A translation method according to any one of claims 1 to 6, wherein said step of translating said data to be translated based on said target language class comprises:
identifying the meaning of the data to be translated based on the target language characteristics of the target language types;
and translating the data to be translated based on the meaning.
8. A translation device, the translation device comprising:
the identification module is used for identifying the minimum record language to which the acquired data to be translated belongs;
the screening module is used for screening out a target language evolution rule of a region corresponding to the minimum record language from a preset language evolution rule library;
the first determining module is used for determining the target language type of the data to be translated directly based on the target language evolution rule, wherein the target language type belongs to the subdivision category in the minimum record language;
and the translation module is used for translating the data to be translated based on the target language type.
9. A translation apparatus, characterized in that the translation apparatus comprises: memory, a processor and a translation program stored on the memory and executable on the processor, the translation program configured to implement the steps of the translation method of any one of claims 1 to 7.
10. A storage medium, characterized in that a program for realizing a translation method is stored on the storage medium, the program for realizing a translation method being executed by a processor to realize the steps of the translation method according to any one of claims 1 to 7.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107945805A (en) * | 2017-12-19 | 2018-04-20 | 程海波 | A kind of intelligent across language voice identification method for transformation |
| WO2019214456A1 (en) * | 2018-05-11 | 2019-11-14 | 深圳双猴科技有限公司 | Gesture language translation system and method, and server |
| CN112329454A (en) * | 2020-11-03 | 2021-02-05 | 腾讯科技（深圳）有限公司 | Language identification method and device, electronic equipment and readable storage medium |
| CN113505609A (en) * | 2021-05-28 | 2021-10-15 | 引智科技(深圳)有限公司 | One-key auxiliary translation method for multi-language conference and equipment with same |
| CN113761885A (en) * | 2021-03-17 | 2021-12-07 | 中科天玑数据科技股份有限公司 | Bayesian LSTM-based language identification method |
| WO2022267394A1 (en) * | 2021-06-25 | 2022-12-29 | 上海博泰悦臻网络技术服务有限公司 | Translation method, and electronic device and readable storage medium |
-
2023
- 2023-08-07 CN CN202310982878.9A patent/CN116702801B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107945805A (en) * | 2017-12-19 | 2018-04-20 | 程海波 | A kind of intelligent across language voice identification method for transformation |
| WO2019214456A1 (en) * | 2018-05-11 | 2019-11-14 | 深圳双猴科技有限公司 | Gesture language translation system and method, and server |
| CN112329454A (en) * | 2020-11-03 | 2021-02-05 | 腾讯科技（深圳）有限公司 | Language identification method and device, electronic equipment and readable storage medium |
| CN113761885A (en) * | 2021-03-17 | 2021-12-07 | 中科天玑数据科技股份有限公司 | Bayesian LSTM-based language identification method |
| CN113505609A (en) * | 2021-05-28 | 2021-10-15 | 引智科技(深圳)有限公司 | One-key auxiliary translation method for multi-language conference and equipment with same |
| WO2022267394A1 (en) * | 2021-06-25 | 2022-12-29 | 上海博泰悦臻网络技术服务有限公司 | Translation method, and electronic device and readable storage medium |
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