Digital translation quality evaluation method and system

Abstract

The invention provides a digital translation quality evaluation method and system. The digital translation quality evaluation scheme provided by the invention mainly relates to two stages, namely an offline training stage and an online evaluation stage. Wherein in the offline training stage, annotation data is mainly utilized to train a digital phrase embedding network, and in the online evaluation stage, the trained digital phrase embedding network is mainly utilized to carry out vector extraction on digital phrases, and a feature library matching mode is utilized to judge the types of the digital phrases; and then digital translation format conversion and translation result evaluation are carried out.

Description

Technical Field

[0001] The present invention relates to the field of natural language processing, and more specifically, to a digital translation quality assessment method and system. Background Art

[0002] For example, in the context of global vehicle manufacturing, professional documents such as operating manuals, instructions, internal specifications, design documents, engineering drawings, etc. need to be accurately translated between different languages. In particular, the numbers involved in professional documents (for example, version numbers, chapter numbers, quantity representations, etc.) are very important, and small differences in numbers may cause technical deviations. In order to translate such professional documents quickly and efficiently, a large number of multilingual translation text pairs are generated. These translation texts have great commercial value for subsequent reuse. However, due to the various factors that come from the sources of the translation pairs currently generated, most of them have uneven quality, so the evaluation of translation quality has received widespread attention in the field of natural language processing. Manual quality evaluation of translated texts will be a huge workload, and multilingual translation involves professional usage of different languages, so professional personnel are required to complete the evaluation work.

[0003] In addition, since the representation of numbers may be different in different languages, a fully automatic method for evaluating the translation of numbers is needed. Summary of the invention

[0004] This Summary is provided to introduce some concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.

[0005] According to one aspect of the present invention, a digital translation quality assessment method is provided, comprising: receiving a translation pair including a source sentence and a target sentence, wherein the source sentence is a sentence to be translated written in a source language, and the target sentence is a sentence corresponding to the source sentence and translated into a target language; extracting numeric phrases in the source sentence and the target sentence to obtain extracted source sentence numeric phrases and extracted target sentence numeric phrases; identifying numeric phrase categories of the source sentence numeric phrases; formatting the numbers in the source sentence numeric phrases based on the identified numeric phrase categories, the source language, and the target language; matching the converted numbers with the numbers in the target sentence numeric phrases; and calculating a digital translation quality assessment score based on the matching results.

[0006] According to a further embodiment of the present invention, the method further comprises: performing data preprocessing on the translation pair to determine whether the target sentence in the translation pair has a predefined translation error.

[0007] According to a further embodiment of the present invention, the predefined translation error includes one of the following: the target sentence in the translation pair is empty; or the target sentence is completely equal to the source sentence.

[0008] According to a further embodiment of the present invention, the number phrase consists of the number itself and a number of characters before and / or after the number.

[0009] According to a further embodiment of the present invention, identifying the digital phrase category of the source sentence digital phrase further includes: obtaining the source data digital phrase feature vector of the source sentence digital phrase through a trained digital phrase embedding network; and matching the source data digital phrase feature vector with the digital phrase feature vector in a feature library to take the digital phrase category corresponding to the most similar digital phrase feature vector in the feature library as the digital phrase category of the source data digital phrase.

[0010] According to a further embodiment of the present invention, the trained number phrase embedding network is capable of evaluating the similarity between the two number phrases based on the feature vectors of the two number phrases.

[0011] According to a further embodiment of the present invention, the feature library records the number phrase feature vectors of one or more number phrases and the corresponding number phrase categories.

[0012] According to another aspect of the present invention, a digital translation quality assessment system is provided, comprising: a trained digital phrase embedding network and a digital translation quality assessment module; the trained digital phrase embedding network is capable of evaluating the similarity between the two digital phrases based on their feature vectors; the digital translation quality assessment module is configured to: receive a translation pair including a source sentence and a target sentence, wherein the source sentence is a sentence to be translated written in a source language, and the target sentence is a sentence corresponding to the source sentence and translated into a target language; extract digital phrases in the source sentence and the target sentence to obtain the extracted source sentence digital phrases and the extracted target sentence digital phrases; identify the digital phrase category of the source sentence digital phrases; format-convert the numbers in the source sentence digital phrases based on the identified digital phrase category, the source language and the target language; match the converted numbers with the numbers in the target sentence digital phrases; and calculate a digital translation quality assessment score based on the matching result of the matching.

[0013] According to a further embodiment of the present invention, the system further comprises: a data preprocessing module, wherein the data preprocessing module is configured to perform data preprocessing on the translation pair to determine whether the target sentence in the translation pair has a predefined translation error.

[0014] According to a further embodiment of the present invention, the predefined translation error includes one of the following: the target sentence in the translation pair is empty, or the target sentence is completely equal to the source sentence.

[0015] According to a further embodiment of the present invention, identifying the digital phrase category of the source sentence digital phrase further includes: obtaining the source data digital phrase feature vector of the source sentence digital phrase through a trained digital phrase embedding network; and matching the source data digital phrase feature vector with the digital phrase feature vector in a feature library to take the digital phrase category corresponding to the most similar digital phrase feature vector in the feature library as the digital phrase category of the source data digital phrase, wherein the feature library records the digital phrase feature vectors and corresponding digital phrase categories of one or more digital phrases.

[0016] According to a further embodiment of the present invention, the number phrase consists of the number itself and a number of characters before and / or after the number.

[0017] According to a further embodiment of the present invention, the feature library records the number phrase feature vectors of one or more number phrases and the corresponding number phrase categories.

[0018] According to another aspect of the present invention, there is provided a digital translation quality assessment system, comprising: an offline training subsystem, the offline training subsystem being configured to train a digital phrase embedding network using annotated digital phrase pairs in different languages, wherein the annotated digital phrase pairs include annotated digital phrase pairs with the same category and annotated digital phrase pairs with different categories; and an online assessment subsystem, the online assessment subsystem comprising the digital translation quality assessment module as described above.

[0019] According to a further embodiment of the present invention, the trained number phrase embedding network is capable of evaluating the similarity between the two number phrases based on the feature vectors of the two number phrases.

[0020] These and other features and advantages will become apparent by reading the following detailed description and by reference to the associated drawings.It is to be understood that the foregoing general description and the following detailed description are illustrative only and are not restrictive of the aspects of what is claimed. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to understand the manner in which the above features of the present invention are used in detail, the above briefly summarized contents can be described in more detail with reference to various embodiments, some of which are shown in the accompanying drawings. However, it should be noted that the accompanying drawings only show some typical aspects of the present invention and should not be considered to limit its scope, because the description may allow for other equally effective aspects.

[0022] Figure 1 A schematic diagram of a system 100 for digital translation quality assessment according to an embodiment of the present invention is shown;

[0023] Figure 2 An embodiment of the present invention is shown. Figure 1 A data stream for performing the evaluation of the digital translation quality evaluation 120;

[0024] Figure 3 The data flow of the digital phrase category recognition process according to one embodiment of the present invention is shown;

[0025] Figure 4 A flowchart of a digital translation quality assessment method 400 according to an embodiment of the present invention is shown;

[0026] Figure 5 A block diagram of an exemplary computing device according to one embodiment of the present invention is shown. DETAILED DESCRIPTION

[0027] The present invention will be described in detail below in conjunction with the accompanying drawings, and the features of the present invention will be further revealed in the following specific description.

[0028] The following detailed description refers to the accompanying drawings showing exemplary embodiments of the present invention. However, the scope of the present invention is not limited to these embodiments, but is defined by the appended claims. Therefore, embodiments other than those shown in the drawings, such as modified versions of the illustrated embodiments, are still encompassed by the present invention.

[0029] References in this specification to "one embodiment," "an embodiment," "an example embodiment," etc., mean that the embodiment may include a particular feature, structure, or characteristic, but not every embodiment necessarily includes the particular feature, structure, or characteristic. Furthermore, these phrases do not necessarily refer to the same embodiment. Furthermore, when a particular feature, structure, or characteristic is described in conjunction with an embodiment, it should be understood that the particular feature, structure, or characteristic can be implemented in conjunction with other embodiments within the knowledge of a person skilled in the relevant art, whether or not explicitly described.

[0030] Unless otherwise stated, the term "A or B" used throughout this specification refers to "A and B" and "A or B" but does not mean that A and B are exclusive.

[0031] Source sentence and target sentence: In the context of this application, a source sentence is a sentence written in a source language to be translated. A target sentence is a sentence corresponding to the source sentence and translated into a target language. A source sentence and a corresponding target sentence constitute a translation pair.

[0032] Numeric phrase: In the context of the present application, a number itself and a number of characters before and / or after the number can form a numeric phrase, for example, "V 6.0", where "6.0" is the number itself, and the two characters "V" (i.e., V and a space) before the number can form a numeric phrase representing the version. Another example is "2.5", where "2.5" is the number itself, and a character "" (i.e., a space) before and after the number can form a numeric phrase, which can represent, for example, a chapter number, etc. Of course, depending on the different numeric phrase categories, a numeric phrase can be formed in a variety of different ways to fully represent the number itself and the numeric category.

[0033] In the prior art, the traditional rule-based matching method cannot intelligently distinguish different digital types, and cannot perform format conversion after translation. The present invention proposes a method based on feature library matching to automatically evaluate the quality of digital translation.

[0034] Specifically, the digital translation quality assessment scheme provided by the present invention mainly involves two stages, namely, an offline training stage and an online assessment stage. In the offline training stage, the digital phrase embedding network is mainly trained using annotated data, while the online assessment stage mainly uses the trained digital phrase embedding network to extract the vector of the digital phrase, uses the feature library matching method to determine the category of the digital phrase, and then performs digital translation format conversion and translation result evaluation.

[0035] Figure 1 FIG. 1 is a schematic diagram of a system 100 for digital translation quality assessment according to an embodiment of the present invention. The system 100 may include an offline training subsystem 105 and an online assessment subsystem 110 .

[0036] According to one embodiment of the present invention, the offline training subsystem 105 is mainly configured to train the digital phrase embedding network so that the vector encodings of digital phrase pairs of the same category have a smaller distance, and the vector encodings of digital phrase pairs of different categories have a larger distance, so that the trained digital phrase embedding network has the ability to evaluate the similarity between two digital phrases.

[0037] For example, referring to the offline training subsystem 105, the training data of the digital phrase embedding network is mainly divided into two categories, one is the labeled digital phrase pairs with the same category, and the other is the labeled digital phrase pairs with different categories. For example, the digital phrase pairs with the same category may include phrase 1 "V 5.0" and phrase 2 "V 2.1", while the digital phrase pairs with different categories may include phrase 1 "V 5.0" and phrase 2 "$1".

[0038] In practice, the training data may contain number-word pairs in multiple (e.g., 15 or even more) languages, so that the number-word embedding network has the ability to evaluate number translations in multiple languages.

[0039] The loss function of the digital phrase embedding network adopts the CosineEmbeddingLoss (Cosine embedding loss) function, and the main formula is as follows:

[0040]

[0041] In the above formula, N represents the number of samples, a number-phrase pair is represented as (v1, v2, y), v1 and v2 represent the embedding vectors of phrase 1 and phrase 2 respectively, and y represents the label. If the two number-phrases have the same category, they are marked as 1, and if the two number-phrases have different categories, they are marked as 0.

[0042] Thus, after training, the trained number phrase embedding network can evaluate the similarity between the two number phrases based on their feature vectors.

[0043] In addition, during the training process, a feature library for digital phrases may be established, which records digital phrase feature vectors of one or more digital phrases and corresponding digital phrase categories. For example, a digital phrase feature vector of a digital phrase may be obtained through a digital phrase embedding network, and the digital phrase feature vector and the corresponding digital phrase category label (for example, the category label may be manually added) may be stored in the feature library to serve as a comparison basis for subsequent identification of the digital phrase category of the digital phrase in the source sentence.

[0044] Of course, those skilled in the art can fully understand that the feature library may not be constructed during the training process, but may be constructed offline at any time.

[0045] According to one embodiment of the present invention, the online evaluation subsystem 110 is configured to perform data preprocessing on a translation pair including a source sentence and a target sentence (e.g., a human translation text of the source sentence), and to perform digital translation quality evaluation on the translation pair after data preprocessing. The digital translation quality evaluation may generally include extracting vectors of digital phrases in the source sentence using a digital phrase embedding network, determining the category of the digital phrase using a feature library matching method, and then performing digital translation form conversion and translation result evaluation.

[0046] Specifically, the online evaluation subsystem 110 includes a data preprocessing module 115 and a digital translation quality evaluation module 120. It is fully understood by those skilled in the art that the division of the above modules is only explained for the purpose of clarity. The functions of one or more of the above modules can be merged into a single module or split into more modules / submodules. And, one or more of the above modules can be implemented in the form of software, hardware or a combination thereof. In addition, the data flow mode between the modules can adopt a mode known in the art, which is not within the discussion of the present invention.

[0047] According to an embodiment of the present invention, the data preprocessing module 115 is configured to detect whether the target sentence has predefined translation errors, so as to reduce excessive subsequent meaningless calculations.

[0048] For example, the data preprocessing module 115 may be configured to: when the target sentence of the translation pair is empty, it may be determined that there is no translation for the source sentence in the translation pair, and thus the translation quality problem may be directly output.

[0049] For another example, the data preprocessing module 115 may be configured to: when the source sentence and the target sentence in the translation pair are equal, it may be determined that the source sentence has not been translated, and thus a translation quality problem may be directly output.

[0050] Thus, if the data pre-processing module 115 outputs a translation quality problem for a translation pair, the translation pair does not need to be transmitted to the digital translation quality assessment module 120 .

[0051] According to one embodiment of the present invention, the digital translation quality assessment module 120 may be configured to perform the following process: (1) extracting numeric phrases from the source sentence and the target sentence in the translation pair; (2) identifying the numeric phrase category of the extracted source sentence numeric phrase; (3) formatting the numbers in the extracted source sentence numeric phrase based on the identified numeric phrase category, source language, and target language; (4) matching the converted numbers with the numbers in the extracted target sentence numeric phrase; (5) calculating a digital translation quality assessment score based on the numeric matching result.

[0052] Specifically, refer to Figure 2, which shows an embodiment of the present invention Figure 1 The digital translation quality assessment 120 in the embodiment is used as a data stream for performing the assessment.

[0053] According to one embodiment of the present invention, Figure 2 The source sentence and the target sentence included in the translation pair shown in can be processed by the data preprocessing module 115 or not.

[0054] According to one embodiment of the present invention, in the process (1) of extracting the numeric phrases in the source sentence and the target sentence in the translation pair, the numeric phrases in the source sentence and the target sentence can be extracted by regularized matching. In the context of the present application, the numeric phrases extracted from the source sentence are referred to as the source sentence numeric phrases, and the numeric phrases extracted from the target sentence are referred to as the target sentence numeric phrases.

[0055] According to one embodiment of the present invention, in process (2) of identifying the category of the digital phrase of the extracted source sentence digital phrase, the feature library constructed above is used to identify the category of the digital phrase. Since the requirements for digital conversion of different categories are different, it is necessary to identify the category of the extracted digital phrase. If a classification network is trained, the classification network model has no scalability. If new categories appear, the model needs to be retrained, and the classification network model may have bottlenecks, resulting in the inability to quickly filter misclassified samples. Therefore, the present invention adopts the following Figure 3 The category of the number phrase is identified by using the number phrase feature matching method described in .

[0056] Specifically, Figure 3 The data flow of the digit phrase category recognition process according to an embodiment of the present invention is shown. First, the source sentence digit phrase extracted from the source sentence is input into the trained digit phrase embedding network to obtain the feature vector of the source sentence digit phrase, and the source sentence digit phrase feature vector is matched with all the recorded digit phrase feature vectors in the feature library, so as to use the digit phrase category corresponding to the most similar digit phrase feature vector in the feature library (for example, the digit phrase feature vector in the feature library with the closest vector distance to the source sentence digit phrase feature vector) as the digit phrase category of the source sentence digit phrase.

[0057] The specific formula for feature matching is as follows:

[0058] C=L Max(Cos(P1,PD))

[0059] C represents the final output digital phrase category, L represents the digital phrase category label corresponding to the digital phrase feature vector in the feature library. P1 represents the digital phrase feature vector of the input digital phrase (for example, the digital phrase feature vector of the source sentence), and PD represents all digital phrase feature vectors in the feature library.

[0060] Afterwards, the identified numeric phrase category is output for verification. If the current identification of the numeric phrase category is wrong, the corresponding entry in the feature library can be updated (e.g., manually) to modify the numeric phrase category label in the matched entry in the feature library to the correct category, thereby, the feature library can be updated / maintained in a timely manner.

[0061] According to one embodiment of the present invention, in process (3), the format conversion of the numbers in the extracted source sentence number phrases is performed based on the identified number phrase category, source language and target language. The number format conversion can be performed according to a predefined conversion rule, and the predefined conversion rule can define the conversion rule of the number format between different languages. For example, numbers such as chapter numbers and version numbers do not need to be converted, while decimals and thousandths need to be converted in different needs. For example, the decimal point and thousandths symbols in German are "," and "." respectively, while the decimal point and thousandths symbols in English are "." and "," respectively, so the numbers in the source sentence written in German need to be converted to the format in the target sentence translated into English.

[0062] According to one embodiment of the present invention, in the process (4) of matching the converted numbers with the numbers in the extracted target sentence number phrase, it is determined whether the numbers in the extracted target sentence number phrase are the same as the converted data obtained in the process (3). If they are different, it can be identified as a number translation error.

[0063] According to one embodiment of the present invention, in process (5) of calculating the digital translation quality assessment score based on the digital matching result, the digital translation quality assessment score may be calculated based on the following formula:

[0064]

[0065] S 数字 is the digital translation quality assessment score, N num represents the number of digits in the target sentence, E num The number of digits representing the error.

[0066] According to one embodiment of the present invention, after process (5), a list of digital translation errors may be further output to indicate which numbers have translation errors.

[0067] Figure 4FIG. 4 is a flow chart of a digital translation quality assessment method 400 according to an embodiment of the present invention. The method 400 may be executed by the online assessment subsystem 110 .

[0068] At 405 , a translation pair is received, the translation pair comprising a source sentence and a target sentence, wherein the source sentence is a sentence to be translated written in a source language, and the target sentence is a sentence corresponding to the source sentence and translated into a target language.

[0069] At 410, data preprocessing is performed on the translation pair to determine whether the target sentence in the translation pair has a predefined translation error. If yes, the process proceeds to step 415, where a translation error reminder is directly output. If no, the process proceeds to step 420.

[0070] According to one embodiment of the present invention, step 410 may further include comparing the source language and the target language in the translation pair. According to one embodiment of the present invention, the predefined translation errors may include any of the following: the target sentence in the translation pair is empty; the target sentence is completely equal to the source sentence.

[0071] According to one embodiment of the present invention, step 410 may also be omitted, that is, the process may proceed directly from step 405 to step 420 .

[0072] At 420 , the number phrases in the source sentence and the target sentence in the translation pair are extracted to obtain extracted source sentence number phrases and extracted target sentence number phrases.

[0073] If the number of extracted target sentence number phrases is greater than 1, the following steps 425 - 435 may be iteratively performed for each target sentence number phrase.

[0074] At 425, the digital phrase category of the extracted source sentence digital phrase is identified. According to one embodiment of the present invention, step 425 further includes: obtaining a source data digital phrase feature vector of the source sentence digital phrase through a trained digital phrase embedding network, and matching the source data digital phrase feature vector with a digital phrase feature vector in a feature library, so as to use the digital phrase category corresponding to the most similar digital phrase feature vector in the feature library (for example, a digital phrase feature vector in the feature library having the closest vector distance to the feature vector) as the digital phrase category of the source data digital phrase.

[0075] According to one embodiment of the present invention, the feature library may be updated to a correct category based on the identification category error.

[0076] At 430 , the numbers of the extracted source sentence number phrases are formatted based on the identified number phrase category, the source language, and the target language.

[0077] At 435, the converted numbers are matched with the numbers in the extracted target sentence number phrases. According to one embodiment of the present invention, if the converted numbers do not match the numbers in the extracted target sentence number phrases, it can be determined that the number translation is wrong.

[0078] At 440 , a numerical translation quality assessment score is calculated based on the numerical matching results.

[0079] In summary, the present invention introduces a feature matching scheme in the digital translation quality assessment, making the matching method more flexible. There is no need to redesign and train the network, and only the feature library needs to be maintained to dynamically identify different types of numbers. Therefore, there is no restriction on the identified categories, and new categories can be added more conveniently.

[0080] Figure 5 A block diagram of an exemplary computing device according to one embodiment of the present invention is shown, which is one example of a hardware device applicable to various aspects of the present invention.

[0081] refer to Figure 5 , a computing device 500 will now be described, which is an example of a hardware device applicable to various aspects of the present invention. The computing device 500 can be any machine that can be configured to perform processing and / or computing, and can be but is not limited to a workstation, a server, a desktop computer, a laptop computer, a tablet computer, a personal digital assistant, a smart phone, a vehicle-mounted computer, or any combination thereof. The various methods / apparatus / server / client devices described above can be implemented in whole or in part by the computing device 500 or a similar device or system.

[0082] The computing device 500 may include components that may be connected or communicated via one or more interfaces and a bus 502. For example, the computing device 500 may include a bus 502, one or more processors 504, one or more input devices 506, and one or more output devices 508. The one or more processors 504 may be any type of processor and may include, but are not limited to, one or more general-purpose processors and / or one or more special-purpose processors (e.g., a dedicated processing chip). The input device 506 may be any type of device capable of inputting information to the computing device and may include, but are not limited to, a mouse, a keyboard, a touch screen, a microphone, and / or a remote controller. The output device 508 may be any type of device capable of presenting information and may include, but are not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The computing device 500 may also include or be connected to a non-transient storage device 510, which may be any storage device that is non-transient and capable of data storage, and may include, but is not limited to, a disk drive, an optical storage device, a solid-state memory, a floppy disk, a floppy disk, a hard disk, a tape or any other magnetic medium, an optical disk or any other optical medium, a ROM (read-only memory), a RAM (random access memory), a cache memory, and / or any memory chip or cassette, and / or any other medium from which a computer can read data, instructions, and / or code. The non-transient storage device 510 may be detachable from the interface. The non-transient storage device 510 may have data / instructions / code for implementing the above methods and steps. The computing device 500 may also include a communication device 512. The communication device 512 can be any type of device or system that can communicate with internal devices and / or with a network and can include but is not limited to a modem, a network card, an infrared communication device, a wireless communication device and / or a chipset, such as a Bluetooth device, an IEEE 1302.11 device, a WiFi device, a WiMax device, a cellular communication device and / or the like.

[0083] When the computing device 500 is used as an in-vehicle device, it may also be connected to external devices (e.g., a GPS receiver, sensors for sensing different environmental data (such as an accelerometer, a wheel speed sensor, a gyroscope, etc.)). In this way, the computing device 500 may, for example, receive positioning data and sensor data indicating the vehicle's form condition. When the computing device 500 is used as an in-vehicle device, it may also be connected to other devices (e.g., an engine system, wipers, an anti-lock brake system, a vehicle climate setting system, etc.) for controlling the travel and operation of the vehicle (e.g., vehicle climate setting).

[0084] In addition, the non-transitory storage device 510 may have map information and software components so that the processor 504 can implement route guidance processing. In addition, the output device 506 may include a display for displaying a map, displaying a positioning mark of the vehicle, and displaying an image indicating the driving status of the vehicle. The output device 506 may also include a speaker or a headphone interface for audio guidance.

[0085] The bus 502 may include, but is not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MCA) bus, an Enhanced ISA (EISA) bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus. In particular, for vehicle-mounted devices, the bus 502 may also include a Controller Area Network (CAN) bus or other structures designed for automotive applications.

[0086] The computing device 500 may also include a working memory 514 , which may be any type of working memory capable of storing instructions and / or data that facilitate the operation of the processor 504 and may include, but is not limited to, a random access memory and / or a read-only storage device.

[0087] Software components may be located in the working memory 514, including but not limited to an operating system 516, one or more application programs 518, drivers and / or other data and codes. Instructions for implementing the above methods and steps may be included in the one or more application programs 518, and the modules / units / components of the aforementioned various devices / servers / client devices may be implemented by the processor 504 reading and executing the instructions of the one or more application programs 518.

[0088] It should also be recognized that changes may be made according to specific needs. For example, custom hardware may also be used, and / or specific components may be implemented in hardware, software, firmware, middleware, microcode, hardware description language, or any combination thereof. In addition, connections to other computing devices, such as network input / output devices, etc. may be employed. For example, programming hardware (e.g., programmable logic circuits including field programmable gate arrays (FPGAs) and / or programmable logic arrays (PLAs)) with assembly language or hardware programming languages ​​(e.g., VERILOG, VHDL, C++) may be used to implement part or all of the disclosed methods and devices using logic and algorithms according to the present invention.

[0089] Although various aspects of the present invention have been described so far with reference to the accompanying drawings, the above-described methods, systems and devices are merely examples, and the scope of the present invention is not limited to these aspects, but is limited only by the appended claims and their equivalents. Various components may be omitted or replaced by equivalent components. In addition, the steps may be implemented in an order different from the order described in the present invention. In addition, various components may be combined in various ways. It is also important that, as technology develops, many of the components described may be replaced by equivalent components that appear later.

Claims

1. A digital translation quality assessment method, comprising: receiving a translation pair including a source sentence and a target sentence, wherein the source sentence is a sentence to be translated written in a source language, and the target sentence is a sentence corresponding to the source sentence and translated into a target language; Extracting the numeric phrases in the source sentence and the target sentence to obtain the extracted source sentence numeric phrases and the extracted target sentence numeric phrases; Identify the numeral phrase category of the numeral phrase of the source sentence; converting the format of the numbers in the source sentence number phrase based on the identified number phrase category, the source language and the target language; Matching the converted numbers with the numbers in the target sentence number phrase; as well as A numerical translation quality assessment score is calculated based on the matching results of the matching.

2. The method of claim 1, further comprising: Data preprocessing is performed on the translation pair to determine whether the target sentence in the translation pair has a predefined translation error.

3. The method according to claim 2, characterized in that The predefined translation errors include one of the following: The target sentence in the translation pair is empty; or The target sentence is completely equal to the source sentence.

4. The method according to claim 1, characterized in that The numeric phrase consists of the number itself and a number of characters before and / or after the number.

5. The method according to claim 4, characterized in that Identifying the digit phrase category of the digit phrase in the source sentence further includes: Obtaining a source data digit phrase feature vector of the source sentence digit phrase through a trained digit phrase embedding network; and The source data digital phrase feature vector is matched with the digital phrase feature vector in the feature library, so that the digital phrase category corresponding to the most similar digital phrase feature vector in the feature library is used as the digital phrase category of the source data digital phrase.

6. The method according to claim 5, characterized in that The trained number phrase embedding network is capable of evaluating the similarity between the two number phrases based on their feature vectors.

7. The method according to claim 5, characterized in that The feature library records the digital phrase feature vectors of one or more digital phrases and the corresponding digital phrase categories.

8. A digital translation quality assessment system comprising: a trained number phrase embedding network capable of evaluating the similarity between the two number phrases based on their feature vectors; as well as A digital translation quality assessment module, wherein the digital translation quality assessment module is configured to: receiving a translation pair including a source sentence and a target sentence, wherein the source sentence is a sentence to be translated written in a source language, and the target sentence is a sentence corresponding to the source sentence and translated into a target language; Extracting the numeric phrases in the source sentence and the target sentence to obtain the extracted source sentence numeric phrases and the extracted target sentence numeric phrases; Identify the numeral phrase category of the numeral phrase of the source sentence; converting the format of the numbers in the source sentence number phrase based on the identified number phrase category, the source language and the target language; Matching the converted numbers with the numbers in the target sentence number phrase; as well as A numerical translation quality assessment score is calculated based on the matching results of the matching.

9. The system of claim 8, further comprising: A data preprocessing module is configured to perform data preprocessing on the translation pair to determine whether the target sentence in the translation pair has a predefined translation error.

10. The system according to claim 8, characterized in that The predefined translation error includes one of the following: the target sentence in the translation pair is empty, or the target sentence is completely equal to the source sentence.

11. The system according to claim 8, characterized in that Identifying the digit phrase category of the digit phrase in the source sentence further includes: Obtaining a source data digit phrase feature vector of the source sentence digit phrase through a trained digit phrase embedding network; and The source data digital phrase feature vector is matched with the digital phrase feature vector in the feature library, so that the digital phrase category corresponding to the most similar digital phrase feature vector in the feature library is used as the digital phrase category of the source data digital phrase.

12. The system of claim 8, wherein: The numeric phrase consists of the number itself and a number of characters before and / or after the number.

13. The system of claim 12, wherein: The feature library records the digital phrase feature vectors of one or more digital phrases and the corresponding digital phrase categories.

14. A digital translation quality assessment system comprising: An offline training subsystem configured to train a digital phrase embedding network using labeled digital phrase pairs in different languages, wherein the labeled digital phrase pairs include labeled digital phrase pairs with the same category and labeled digital phrase pairs with different categories; as well as An online evaluation subsystem, wherein the online evaluation subsystem comprises a digital translation quality evaluation module as claimed in any one of claims 8 to 13.

15. The system of claim 14, wherein: The trained number phrase embedding network is able to evaluate the similarity between the two number phrases based on their feature vectors.

Images