Patent Application
20210089714
The disclosure relates to translation of text between languages. The disclosure more particularly relates to the combination of machine translation and crowdsourced human translation.
Applications are designed to be consumed by a global audience. To meet that global audience, applications are designed to be translated into multiple languages. Translating many lines of text is a tedious activity. Machine translation presents an efficient method to translate large portions of text into many languages; however, machine translation is prone to generating numerous translation errors that cause an unprofessional look and feel to applications.
Various embodiments or examples (“examples”) of the invention are disclosed in the following detailed description and the accompanying drawings:
Disclosed herein is a translation platform making use of both machine translation and crowd sourced manual translation. A set of text is first translated using a machine translation process, then is subjected to user corrections. Translation is performed on resource files within in an application. The end-client application includes a graphic user interface that enables the user to submit corrections to select portions of text. Manual translations are applied immediately to local versions of the client application and are either human reviewed or reverse machine translated and compared against the original text. Once verified, the translations are applied to all end-clients.
The applications server 32 further includes language packages 32 to support multiple language displays of individual client applications 24. In some embodiments, the language packages 32 are stored with/part of the client application 24. The applications server further includes a lexicon file 34. The machine translation service 36 may be a system operating on the application server 22, or is a service that the users of the application server 22 do not have administrator access to.
The lexicon file 34 is an instructional file for a machine translation service 36. The lexicon file 34 includes instructions to translate particular words (especially industry specific or highly technical terms) in a certain manner in various languages. The lexicon file 34 may be a domain specific dictionary that includes idioms, or terms that do not literally translate well. For example, in drafting contracts, the English language refers to routine or generic language as “boilerplate”. Boilerplate does not translate literally in many other languages. At the time of this disclosure, Google Translate machine translates “boilerplate” into Spanish as “repetitivo” (“Repetitive” in English). A more accurate translation of “boilerplate” into Spainish is “texto estandarizado.” The lexicon file 34 includes relevant industry/technical term translations that the machine translation service 36 can use to more effectively translate.
In operation the application translation network 20 operates the client application 24 on end user devices 26 and is supported by the application server 22 as a backend. The given application content and purpose may vary, and at a minimum, includes translatable text. For example, the client application 24 may be a game, a social network, accounting software, business flow software/enterprise software, or even a combination thereof. The client software 24 includes a setting to adjust the language of text of pages (and by extension, resource files therein) within the application 24. When a user toggles the language setting the client application accesses the relevant language pack 32 (for the selected language). The language pack 32 is initially populated by a machine translation of the original text of the application page. In some embodiments, the machine translation is influenced by the lexicon file 34.
In the given example, the original English phrase is not a complete sentence and thus machine translation is often complicated. In some embodiments, the grammar of machine translation improves with additional original text. The completeness of a sentence or passage influences the accuracy of some machine translation engines.
In
In step 404, the application server submits the extracted text to a machine translation service. There are a number of machine translation services available. Many are freely accessible on the Internet. Examples include services offered by Google and IBM Watson. The machine translator used may vary based on the text itself. Prior to submission to the translator, the application server analyzes the extracted text segment. Analysis may include word count and/or inclusion of terms included in the lexicon file. Some machine translators function better than others when the content of the text to be translated is longer. The inclusion of additional context improves the ability of the AI used to translate. Some machine translators function better than others when a lexicon file can be effectively applied to the particular text segment. Thus, a machine translator is chosen based on the result of the analysis of the extracted passage of text. The machine translator used is predetermined based on various potential outcomes of the analysis. The analysis may use thresholds and/or pattern matching to arrive at an outcome.
In step 406, The application server receives the translated text from the machine translator. In step 408, The application server generates a second version of the application page from which the extracted text was from. The second version uses the machine translated text placed in the same location as the corresponding text in the original language. In some embodiments, the steps 402-406 are performed a number of times prior to the performance of step 408 in order to machine translate each string of text on the first version of the application page. The second version of the page is navigated to within the client application in the same manner as the first page. The primary distinction between the first and second version of the page is the translated text. In some embodiments, the second version of the page includes the translation of less than all text fields/resource files in a given page.
In step 410, a user of a client application submits corrections to the machine translated text of the second page to the application server. The submitted corrections may pertain to all or part of text or text segments of the second page. The corrections are transmitted from the end-client application to the application server backend. Step 410a occurs simultaneously and out of band with the application server. When the user supplied corrections through the user interface of the client application, the client application immediately applies the corrections to that particular user's client application.
In step 412, the application server determines whether the corrections are valid. Determination of validity may be made via human inspection or machine validation. Human inspection includes receipt by an administrator of the application server. Machine validation may include a reverse translation process. Reverse translation includes machine translating the correction back into the original language (for example, from French back into English) and then conducting a comparison to the original text. Machine validation may further use a confidence score for corrections based on a number of criteria such as: the number of identical submissions for the same correction from different participants, the number of accepted submissions from a participant, the strength and nature of the relationship with the participant, and the reverse translation comparison already included in the draft.
Where the corrections are validated, in step 414, the application server applies the corrections across all instances of the second version of the application page (in the second language). In some embodiments, certain local versions of the client application will not update. The original submitter of the correction will not require an update because the user already updated their own local version (during step 410a). Additionally, some users may deactivate updates in application settings. In this manner, the machine translations are updated by the user base of the client application.
In some embodiments, the users whom are able to submit corrections are limited to those within a predetermined class of higher tier users. Some users of the client application may have more permissions than other users. Permissions may pertain to the ability to correct language.
Deferring the quality control of translations onto the client applications is an improvement to the processing efficiency of the system. Client application translation correction relies on cloud computing principles in order to defray the computational cost of translations.
In step 508, the application server compares the result of step 506 with the original text from the first language. The comparison uses character thresholds to determine a degree of accuracy between the reverse translated text and the original text. In some embodiments the character match threshold analyzes both the inclusion of characters and the positioning of those characters. Analysis of positioning determined on a per word basis (e.g., the order of the words “and anaconda” as opposed to “anaconda and” rather than that location of the letter A throughout each word). The strings should ideally match. In step 510, the application server determines whether the correction is valid based on the comparison of the reverse translated correction to the original text. If the correction is valid based on the result of step 508, then in step 512, the correction is applied across the application server.
Where the determination of step 510 is invalid, an additional layer of comparison is performed. In step 514, the application server determines if there is a potential reverse translation issue present. In some cases, words don't translate directly.
For example, in English, the finger after the middle finger is referred to as the “ring finger” (as this is the finger a wedding ring is traditionally worn on). In Russian, that same finger is literally called the “unnamed finger.” When first translated, ring finger becomes the Russian word for unnamed finger (). When reverse translated back into English, the result is “unnamed finger.” The result will not pass a comparison test. Thus, the ring finger example, as well as many others (there is also no word for “toe” in Russian), will cause a reverse translation issue. In order to evaluate whether a reverse translation issue exists, the application server machine translates the original text to the second language and then reverse machine translates (e.g., without any human correction).
If the result of the machine reverse translation does not match the original, there is a reverse translation issue. Where there is no reverse translation issue, in step 516, the corrections are discarded at the backend server level. In some embodiments, the corrections are also removed from the submitting user's end-client application.
Where a reverse translation issue is detected, in step 518, the application server compares the result of the reverse translation of the original and the reverse translation of the correction. The application server assumes that there will be greater variation between the two results and between the comparison of step 508. The variation is a result of the necessity of a correction in the first place. The correction ostensibly occurred because the machine translation from the original was incorrect. The comparison of step 518, performs both a character match threshold, and a thesaurus analysis. A thesaurus analysis compares whether the result of the reverse translation of the corrected text includes synonyms of the pure machine translation and reverse translation of the original text. Identification of synonyms increases a match threshold score. With respect to the character match threshold comparison of step 518, the application server only compares the presence of the characters and not the positioning.
In step 520, the application server determines the validity of the correction based on the comparison of step 518. Where the correction is invalid based on the step 518 analysis, in step 516, the correction is discarded.
In step 702, the application server extracts text from the application page. In step 704 the application server submits the extracted text to a first machine translation service. Concurrently, in step 706, the application server submits the same extracted text to other machine translation services (a second service, a third service, up to the Nth service). Different machine translation services may have varied effectiveness at varied segments of text. Within the same segment of text different machine translation services may function at varying levels of accuracy.
In step 708, the application server determines a method to validate the various output of the N machine translations services. Based on application server settings and functionality, the machine translation output is validated by human input or a fully automated process.
Where the application server makes use of a fully automated process, in step 710, the application server compares the N machine translations. The comparison of the machine translations includes the use of a word processor to determine the readability of the machine translated text. The word processor assigns a confidence score to each machine translation and each portion of the machine translation therein. Where a majority of the machine translations are in agreement the confidence score is high. Where the word processor detects grammatical errors, the confidence level is low. The word processor further uses natural language processing to determine whether a word is out of place. For example, where an adjective appears when a noun is expected, confidence in the machine translation is lower.
In step 712, the various machine translations are reconciled into a single, composite machine translation. Reconciliation of machine translations may use the entirety of a single machine translation or pick and choose portions of multiple machine translations to assemble a single machine translation with the highest machine confidence. During the reconciliation of the multiple machine translations, an additional word processor analysis is performed on the composite translation in order to prevent the introduction of new errors during compositing. Where the word processor detects new grammatical or contextual errors, the word processor implements a correction using natural language processing.
Where the application server makes use of human validation, in step 714, the application server or end-client application displays multiple machine translations to a user either individually or side-by-side. The user interface requests that the user select a preference. In some embodiments, the user preference may be stored and automatically applied in the future. In step 716, the application server executes the user's preference for a current passage of text. In some embodiments, the application server or the client application may apply the same preference on other passages of text that are machine translated.
In step 718, regardless of how the composite machine translation is arrived upon, the system displays the machine translation of the application page to the user.
The computer 800 may be a standalone device or part of a distributed system that spans multiple networks, locations, machines, or combinations thereof. In some embodiments, the computer 800 operates as a server computer or a client device in a client-server network environment, or as a peer machine in a peer-to-peer system. In some embodiments, the computer 800 may perform one or more steps of the disclosed embodiments in real time, near real time, offline, by batch processing, or combinations thereof.
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The control 704 includes one or more processors 812 (e.g., central processing units (CPUs)), application-specific integrated circuits (ASICs), and/or field-programmable gate arrays (FPGAs), and memory 814 (which may include software 816).
For example, the memory 814 may include volatile memory, such as random-access memory (RAM), and/or non-volatile memory, such as read-only memory (ROM). The memory 714 can be local, remote, or distributed.
A software program (e.g., software 816), when referred to as “implemented in a computer-readable storage medium,” includes computer-readable instructions stored in the memory (e.g., memory 814). A processor (e.g., processor 812) is “configured to execute a software program” when at least one value associated with the software program is stored in a register that is readable by the processor. In some embodiments, routines executed to implement the disclosed embodiments may be implemented as part of an operating system (OS) software (e.g., Microsoft Windows® and Linux®) or a specific software application, component, program, object, module, or sequence of instructions referred to as “computer programs.”
As such, the computer programs typically comprise one or more instructions set at various times in various memory devices of a computer (e.g., computer 800), which, when read and executed by at least one processor (e.g., processor 812), will cause the computer to perform operations to execute features involving the various aspects of the disclosed embodiments. In some embodiments, a carrier containing the aforementioned computer program product is provided. The carrier is one of an electronic signal, an optical signal, a radio signal, or a non-transitory computer-readable storage medium (e.g., memory 814).
The network interface 806 may include a modem or other interfaces (not shown) for coupling the computer 800 to other computers over the network 20. The I/O system 808 may operate to control various I/O devices, including peripheral devices, such as a display system 818 (e.g., a monitor or touch-sensitive display) and one or more input devices 720 (e.g., a keyboard and/or pointing device). Other I/O devices 722 may include, for example, a disk drive, printer, scanner, or the like. Lastly, the clock system 720 controls a timer for use by the disclosed embodiments.
Operation of a memory device (e.g., memory 824), such as a change in state from a binary one (1) to a binary zero (0) (or vice versa) may comprise a visually perceptible physical change or transformation. The transformation may comprise a physical transformation of an article to a different state or thing. For example, a change in state may involve accumulation and storage of charge or a release of stored charge. Likewise, a change of state may comprise a physical change or transformation in magnetic orientation or a physical change or transformation in molecular structure, such as a change from crystalline to amorphous or vice versa.
Aspects of the disclosed embodiments may be described in terms of algorithms and symbolic representations of operations on data bits stored in memory. These algorithmic descriptions and symbolic representations generally include a sequence of operations leading to a desired result. The operations require physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electric or magnetic signals that are capable of being stored, transferred, combined, compared, and otherwise manipulated. Customarily, and for convenience, these signals are referred to as bits, values, elements, symbols, characters, terms, numbers, or the like. These and similar terms are associated with physical quantities and are merely convenient labels applied to these quantities.
While embodiments have been described in the context of fully functioning computers, those skilled in the art will appreciate that the various embodiments are capable of being distributed as a program product in a variety of forms and that the disclosure applies equally, regardless of the particular type of machine or computer-readable media used to actually effect the embodiments.
While the disclosure has been described in terms of several embodiments, those skilled in the art will recognize that the disclosure is not limited to the embodiments described herein and can be practiced with modifications and alterations within the spirit and scope of the invention. Those skilled in the art will also recognize improvements to the embodiments of the present disclosure. All such improvements are considered within the scope of the concepts disclosed herein. Thus, the description is to be regarded as illustrative instead of limiting.
From the foregoing, it will be appreciated that specific embodiments of the invention have been described herein for purposes of illustration, but that various modifications may be made without deviating from the scope of the invention. Accordingly, the invention is not limited except as by the appended claims.
This application is a continuation of copending U.S. patent application Ser. No. 16/027,207, filed Jul. 3, 2018 and entitled, “ARTIFICIAL INTELLIGENCE AND CROWDSOURCED TRANSLATION PLATFORM,” all of which is herein incorporated by reference in its entirety for all purposes.
| Number | Date | Country | |
|---|---|---|---|
| Parent | 16027207 | Jul 2018 | US |
| Child | 17039852 | US |
