1. A method of text translation, comprising:

acquiring corresponding K sentence pairs aiming at a first sentence to be translated, wherein K is a positive integer larger than one, each sentence pair respectively comprises a source language sentence and a corresponding target language sentence, the source language is a language corresponding to the first sentence, and the target language is a language corresponding to a translation result of the first sentence;

and respectively generating a translation result according to each sentence pair, and taking the obtained K translation results as the translation result of the first sentence.

2. The method of claim 1, wherein said obtaining corresponding K sentence pairs comprises:

grouping the sentence pairs serving as the selection objects, wherein the lengths of the source language sentences in the sentence pairs in the same group are the same, or the lengths of the source language sentences in the sentence pairs in the same group are in the same value range;

and determining the groups matched with the first sentence according to the length of the first sentence, and selecting K sentence pairs from the matched groups.

3. The method of claim 2, wherein said selecting K sentence pairs from said matched groups comprises:

selecting K sentence pairs from the matched groups according to the principle that the average value of cosine similarity between source language sentences in every two different sentence pairs is minimum;

or randomly selecting K sentence pairs from the matched groups.

4. The method of claim 1, wherein the generating a translation result from each sentence pair comprises:

and aiming at any sentence pair, respectively generating a translation result according to the sentence pair and the first sentence through a translation algorithm based on an encoding and decoding architecture.

5. The method of claim 4, wherein said generating a translation result from said sentence pair and said first sentence comprises:

in the encoding stage, for each word instance token in the first sentence, the following processing is respectively performed:

taking the token as a token to be processed;

embedding the word of the token to be processed into a word embedding representation and carrying out weighted addition on the word embedding representation and the word embedding representation of the token in the source language sentence in the sentence pair;

and taking the weighted addition result as the word embedding of the token to be processed required by the subsequent processing.

6. The method of claim 4, wherein said generating a translation result from said sentence pair and said first sentence comprises:

in the decoding stage, the following processes are respectively performed for each newly generated word example token:

carrying out weighted addition on the word embedding representation word embedding of the newly generated token and the word embedding of the token in the target language sentence in the sentence pair;

and taking the weighted addition result as the word embedding of the newly generated token required by the subsequent processing.

7. The method of claim 5 or 6, further comprising:

respectively setting weights used when the sentences are added to corresponding weights according to a preset principle, wherein the preset principle comprises the following steps: the closer the distance between the source language sentence and the first sentence is, the larger the corresponding weight is, and conversely, the smaller the corresponding weight is.

8. A text translation apparatus comprising: an acquisition module and a translation module;

the obtaining module is used for obtaining corresponding K sentence pairs aiming at a first sentence to be translated, wherein K is a positive integer larger than one, each sentence pair respectively comprises a source language sentence and a corresponding target language sentence, the source language is a language corresponding to the first sentence, and the target language is a language corresponding to a translation result of the first sentence;

and the translation module is used for generating a translation result according to each sentence pair and taking K translation results as the translation result of the first sentence.

9. The apparatus of claim 8, wherein,

the obtaining module groups each sentence pair as a selection object, the source language sentences in each sentence pair in the same group have the same length, or the source language sentences in each sentence pair in the same group have the same length in the same value range, groups matched with the first sentence are determined according to the length of the first sentence, and K sentence pairs are selected from the matched groups.

10. The apparatus of claim 9, wherein,

the acquisition module selects K sentence pairs from the matched groups according to the principle that the average value of cosine similarity between source language sentences in every two different sentence pairs is minimum;

or the acquisition module randomly selects K sentence pairs from the matched groups.

11. The apparatus of claim 8, wherein,

the translation module generates a translation result according to any sentence pair and the first sentence through a translation algorithm based on a coding and decoding framework aiming at the sentence pair.

12. The apparatus of claim 11, wherein,

the translation module carries out the following processing for each word instance token in the first sentence in the encoding stage aiming at any sentence pair: taking the token as a token to be processed; embedding the word of the token to be processed into a word embedding representation and carrying out weighted addition on the word embedding representation and the word embedding representation of the token in the source language sentence in the sentence pair; and taking the weighted addition result as the word embedding of the token to be processed required by the subsequent processing.

13. The apparatus of claim 11, wherein,

the translation module respectively performs the following processing for any sentence pair and for the latest generated word example token at each time in the decoding stage: carrying out weighted addition on the word embedding representation word embedding of the newly generated token and the word embedding of the token in the target language sentence in the sentence pair; and taking the weighted addition result as the word embedding of the newly generated token required by the subsequent processing.

14. The apparatus of claim 12 or 13,

the translation module is further configured to set weights used when weights corresponding to each sentence are added to each other according to a predetermined rule, where the predetermined rule includes: the closer the distance between the source language sentence and the first sentence is, the larger the corresponding weight is, and conversely, the smaller the corresponding weight is.

15. An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.

16. A non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method of any one of claims 1-7.

17. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-7.

Background

Machine translation refers to the process of translating one natural language text (source language sentence) into another natural language text (target language sentence) by the force of a machine.

In recent years, neural network models have made significant progress in the task of machine translation. However, in the current implementation, only a single translation result is usually obtained for a sentence to be translated, and the diversity is poor.

Disclosure of Invention

The disclosure provides a text translation method, a text translation device, an electronic device and a storage medium.

A method of text translation, comprising:

acquiring corresponding K sentence pairs aiming at a first sentence to be translated, wherein K is a positive integer larger than one, each sentence pair respectively comprises a source language sentence and a corresponding target language sentence, the source language is a language corresponding to the first sentence, and the target language is a language corresponding to a translation result of the first sentence;

and respectively generating a translation result according to each sentence pair, and taking the obtained K translation results as the translation result of the first sentence.

A text translation apparatus comprising: an acquisition module and a translation module;

the obtaining module is used for obtaining corresponding K sentence pairs aiming at a first sentence to be translated, wherein K is a positive integer larger than one, each sentence pair respectively comprises a source language sentence and a corresponding target language sentence, the source language is a language corresponding to the first sentence, and the target language is a language corresponding to a translation result of the first sentence;

and the translation module is used for generating a translation result according to each sentence pair and taking K translation results as the translation result of the first sentence.

An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a method as described above.

A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method as described above.

A computer program product comprising a computer program which, when executed by a processor, implements a method as described above.

One embodiment in the above disclosure has the following advantages or benefits: for the first sentence to be translated, corresponding K sentence pairs can be obtained, and a corresponding translation result can be generated according to each sentence pair, so that the translation results of the K first sentences can be obtained, and the diversity of the translation results is improved.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:

FIG. 1 is a flow chart of an embodiment of a text translation method according to the present disclosure;

FIG. 2 is a diagram illustrating the results of the text translation method of the present disclosure;

FIG. 3 is a schematic diagram illustrating an exemplary embodiment 300 of a text translation apparatus according to the present disclosure;

FIG. 4 shows a schematic block diagram of an example electronic device 400 that may be used to implement embodiments of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

In addition, it should be understood that the term "and/or" herein is merely one type of association relationship that describes an associated object, meaning that three relationships may exist, e.g., a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.

Fig. 1 is a flowchart of an embodiment of a text translation method according to the present disclosure. As shown in fig. 1, the following detailed implementation is included.

In step 101, for a first sentence to be translated, K corresponding sentence pairs are obtained, where K is a positive integer greater than one, each sentence pair includes a source language sentence and a corresponding target language sentence, the source language is a language corresponding to the first sentence, and the target language is a language corresponding to a translation result of the first sentence.

In step 102, a translation result is generated from each sentence pair, and K translation results obtained are used as the translation result of the first sentence.

It can be seen that, in the scheme of the embodiment of the method, for the first sentence to be translated, the corresponding K sentence pairs can be obtained, and a corresponding translation result can be generated according to each sentence pair, so that the translation results of the K first sentences can be obtained, and the diversity of the translation results is further improved.

For ease of presentation, the sentence to be translated is referred to as the first sentence. In an embodiment of the present disclosure, to obtain K sentence pairs corresponding to a first sentence, each sentence pair serving as a selection object may be grouped, where lengths of source language sentences in each sentence pair in the same group are the same, or lengths of source language sentences in each sentence pair in the same group are within the same value range, and accordingly, a group matching the first sentence may be determined according to the length of the first sentence, and then K sentence pairs may be selected from the matching group.

The sentence pairs that are the objects of selection may be training data for a machine translation model. Each sentence pair includes a source language sentence and a corresponding target language sentence, respectively.

Assuming that 1 ten thousand sentence pairs exist in total, the 1 ten thousand sentence pairs can be grouped, assuming that the sentence pairs are divided into 20 groups, which are respectively the group 1 to the group 20, taking the group 1 as an example, the lengths of the source language sentences in each sentence pair are the same, or the lengths of the source language sentences in each sentence pair are in the same value range, for example, the lengths are both in the value range of (a, b), b is greater than a, and the specific value can be determined according to actual needs. That is, two groupings may be used, one in which the source language sentences in each sentence pair in the same grouping are the same length, and the other in which the source language sentences in each sentence pair in the same grouping are similar in length. The specific method can be determined according to actual needs.

The groups matched with the first sentence can be determined according to the length of the first sentence, namely the groups corresponding to the length of the first sentence or the groups corresponding to the value range in which the length of the first sentence is located are determined, and then K sentence pairs can be selected from the matched groups.

By the method, the lengths of the sentences in the selected K sentence pairs are ensured to be as close as possible to the length of the first sentence, so that subsequent processing is facilitated, and a good foundation is laid for the subsequent processing.

In one embodiment of the present disclosure, when K sentence pairs are selected from the matched groups, the following manner may be adopted:

1) selecting K sentence pairs from the matched groups according to the principle that the average value of cosine similarity between source language sentences in every two different sentence pairs is minimum;

2) k sentence pairs are randomly selected from the matched groups.

In order to ensure that the subsequently generated K translation results can accurately express the semantics of the first sentence, and can have obvious diversity, the method 1) can be adopted to select K sentence pairs, so that the sentences in the selected K sentence pairs are distinguished from each other as much as possible in the hidden state space.

Correspondingly, a cosine similarity regular term is introduced in the method 1) to constrain cosine similarity between sentences in the selected K sentence pairs, and specifically, the K sentence pairs can be selected from matched groups according to the principle of minimizing the average value of the cosine similarity between source language sentences in every two different sentence pairs.

Namely, the method comprises the following steps:

where x represents an embedded representation (embedding) of the first sentence, xⁱRepresenting embedding of the source language sentence in the ith sentence pair of the selected K sentence pairs, wherein the embedding of the sentences can be obtained by averaging word embedding of tokens in the sentences, and xⁱ-x is such that xⁱNormalized to coordinate space with x as origin, x^jAlso, X¹～X^KRepresenting the source language sentence in the selected K sentence pairs.

The implementation mode of the mode 2) is simple, and after the matched groups are determined, K sentences can be selected randomly from the matched groups directly, so that the operation overhead can be reduced, the selection efficiency can be improved, and the like.

For the K sentence pairs, corresponding translation results can be generated respectively. In one embodiment of the present disclosure, for any sentence pair, a translation result may be generated according to the sentence pair and the first sentence through a translation algorithm based on an encoding and decoding architecture. In the encoding and decoding stages, data mixing can be performed at the embedding level respectively.

The specific algorithm is not limited, and for example, the translation algorithm may be an existing machine translation algorithm based on an encoding + decoding architecture.

In one embodiment of the present disclosure, for any sentence pair, in the encoding stage, the following processing may be performed for each word example (token) in the first sentence respectively: taking the token as a token to be processed, performing weighted addition on word embedding representation (word embedding) of the token to be processed and word embedding of the token corresponding to the token in the source language sentence in the sentence pair, and taking a weighted addition result as the word embedding of the token to be processed required by subsequent processing.

Namely, the method comprises the following steps:

wherein, e (x)_t) Word embedding representing the t-th token in the first sentence, i.e., 1 ≦ t ≦ L, L representing the number of tokens included in the sentence, i representing the ith sentence pair of the K sentence pairs, i.e., 1 ≦ i ≦ K,word embedding, λ, representing the t token in the source language sentence in the ith sentence pair_iThe weight is represented by a weight that is,and (3) word embedding of the t token required by the obtained subsequent processing.

In practical applications, if the lengths of the source language sentences in the first sentence and the ith sentence are different, for example, the number of tokens included in the first sentence and the ith sentence is different, the length of the sentence with the shorter length can be complemented to the length of the sentence with the longer length by a length complementing operation or the like.

In one embodiment of the present disclosure, for any sentence pair, in the decoding stage, for each newly generated token, the following processing may be performed: and carrying out weighted addition on the word embedding of the newly generated token and the word embedding of the corresponding token in the target language sentence in the sentence pair, and taking the weighted addition result as the word embedding of the newly generated token required by the subsequent processing.

Namely, the method comprises the following steps:

wherein, e (y)_t-1) Word embedding representing token generated in step t-1, i represents the ith sentence pair in the K sentence pairs,representing the word embedding, λ, representing the t-1 token in the target language sentence in the ith sentence pair_iThe weight is represented by a weight that is,and (3) word embedding representing the t-1 th token required by the obtained subsequent processing. In addition, e (y)₀) Can be the beginning symbol of a sentence<bos>The word embedding.

Through the processing, data mixing in the embedding level can be respectively realized in the encoding stage and the decoding stage, so that the diversity of the obtained translation results can be improved.

In an embodiment of the present disclosure, weights used when the corresponding weights of the sentence pairs are added may be further set according to a predetermined rule, where the predetermined rule may include: the closer the distance between the source language sentence and the first sentence is, the larger the corresponding weight is, and conversely, the smaller the corresponding weight is.

The method comprises the steps of selecting proper weight according to the distance between a source language sentence and a first sentence in K sentence pairs, wherein the closer the distance is, the higher the corresponding weight is, and the farther the distance is, the smaller the corresponding weight is.

If so, the following steps are available:

λ_i＝Beta(α_i,α_i)； (5)

wherein τ represents a set parameter, a specific value can be determined according to actual needs, x represents embedding of the first sentence, and x representsⁱRepresenting the embedding of the source language sentence in the ith sentence pair of the selected K sentence pairs, wherein the embedding of the sentences can be obtained by averaging the word embedding of each token in the sentences, and d (x, x)ⁱ) Denotes x and xⁱBeta represents a Beta distribution, which includes two parameters,in the scheme of the disclosure, both parameters are alpha obtained in the formula 4)_i。

In practical application, for different tokens in the same sentence, corresponding λ_iMay be the same or different, as long as the beta distribution described above is met. In addition, for different sentence pairs, the corresponding weight generally needs to conform to the principle that the closer the distance, the larger the corresponding weight, and conversely, the farther the distance, the smaller the corresponding weight.

Through the processing, the corresponding weights of different sentence pairs can be accurately set, so that the accuracy of a data mixing result is improved, and the like.

Based on the above processing of the encoding stage and the decoding stage, it is prior art how to perform encoding and decoding and how to generate the corresponding translation result.

In summary, fig. 2 is a schematic diagram illustrating the result of the text translation method according to the present disclosure. As shown in fig. 2, the left large circle represents the hidden state space of the source language sentence, the right large circle represents the hidden state space of the target language sentence, the square in the left large circle represents the first sentence to be translated, the four diamonds represent 4(K ═ 4) sentence pairs respectively, the four small circles represent the results after data mixing respectively, 4 corresponding translation results can be obtained through a translation algorithm, as shown by the rectangles in the right large circle, only one translation result corresponding to the first sentence can be obtained according to the existing processing mode, as shown by the ellipse in the right large circle, and after the method disclosed by the present disclosure is adopted, 4 translation results can be obtained.

It is noted that while for simplicity of explanation, the foregoing method embodiments are described as a series of acts, those skilled in the art will appreciate that the present disclosure is not limited by the order of acts, as some steps may, in accordance with the present disclosure, occur in other orders and concurrently. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required for the disclosure.

The above is a description of embodiments of the method, and the embodiments of the apparatus are further described below.

Fig. 3 is a schematic structural diagram of a composition of an embodiment 300 of a text translation apparatus according to the present disclosure. As shown in fig. 3, includes: an acquisition module 301 and a translation module 302.

An obtaining module 301, configured to obtain, for a first sentence to be translated, K corresponding sentence pairs, where K is a positive integer greater than one, each sentence pair includes a source language sentence and a corresponding target language sentence, the source language is a language corresponding to the first sentence, and the target language is a language corresponding to a translation result of the first sentence.

The translation module 302 is configured to generate a translation result according to each sentence pair, and use K translation results obtained as the translation result of the first sentence.

In an embodiment of the present disclosure, the obtaining module 301 may group the sentence pairs as the selection objects, where the lengths of the source language sentences in the sentence pairs in the same group are the same, or the lengths of the source language sentences in the sentence pairs in the same group are in the same value range, and accordingly, a group matching the first sentence may be determined according to the length of the first sentence, and K sentence pairs may be selected from the matching group.

In an embodiment of the disclosure, the obtaining module 301 may select K sentence pairs from the matched groups according to a principle that an average value of cosine similarities between source language sentences in every two different sentence pairs is minimized, or the obtaining module 301 may randomly select K sentence pairs from the matched groups.

In addition, in an embodiment of the present disclosure, the translation module 302 may generate a translation result according to any sentence pair and the first sentence through a translation algorithm based on an encoding and decoding architecture, respectively.

In one embodiment of the present disclosure, for any sentence pair, the translation module 302 may perform the following processing for each token in the first sentence in the encoding stage: taking the token as a token to be processed; carrying out weighted addition on the word embedding of the token to be processed and the word embedding of the token corresponding to the source language sentence in the sentence pair; and taking the weighted addition result as the word embedding of the token to be processed required by the subsequent processing.

In one embodiment of the present disclosure, for any sentence pair, the translation module 302 may perform the following processing for each newly generated token in the decoding stage: weighting and adding the newly generated word embedding of the token and the word embedding of the corresponding token in the target language sentence in the sentence pair; and taking the weighted addition result as the word embedding of the newly generated token required by the subsequent processing.

In addition, in an embodiment of the present disclosure, the translation module 302 may further set weights used when the corresponding weights of the sentences are added according to a predetermined rule, where the predetermined rule may include: the closer the distance between the source language sentence and the first sentence is, the higher the corresponding weight is, and conversely, the closer the distance between the source language sentence and the first sentence is, the lower the corresponding weight is.

For a specific work flow of the apparatus embodiment shown in fig. 3, reference is made to the related description in the foregoing method embodiment, and details are not repeated.

In a word, by adopting the scheme of the embodiment of the device disclosed by the disclosure, for the first sentence to be translated, the corresponding K sentence pairs can be obtained, and a corresponding translation result can be generated according to each sentence pair, so that the translation results of the K first sentences can be obtained, and the diversity of the translation results and the like are further improved.

The scheme disclosed by the disclosure can be applied to the field of artificial intelligence, in particular to the fields of natural language processing, deep learning and the like.

Artificial intelligence is a subject for studying a computer to simulate some thinking processes and intelligent behaviors (such as learning, reasoning, thinking, planning and the like) of a human, and has a hardware technology and a software technology, the artificial intelligence hardware technology generally comprises technologies such as a sensor, a special artificial intelligence chip, cloud computing, distributed storage, big data processing and the like, and the artificial intelligence software technology mainly comprises a computer vision technology, a voice recognition technology, a natural language processing technology, machine learning/deep learning, a big data processing technology, a knowledge graph technology and the like.

The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.

FIG. 4 shows a schematic block diagram of an example electronic device 400 that may be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 4, the apparatus 400 includes a computing unit 401 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM)402 or a computer program loaded from a storage unit 408 into a Random Access Memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the device 400 can also be stored. The computing unit 401, ROM 402, and RAM 403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.

A number of components in device 400 are connected to I/O interface 405, including: an input unit 406 such as a keyboard, a mouse, or the like; an output unit 407 such as various types of displays, speakers, and the like; a storage unit 408 such as a magnetic disk, optical disk, or the like; and a communication unit 409 such as a network card, modem, wireless communication transceiver, etc. The communication unit 409 allows the device 400 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.

Computing unit 401 may be a variety of general and/or special purpose processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The computing unit 401 performs the various methods and processes described above, such as the methods described in this disclosure. For example, in some embodiments, the methods described in this disclosure may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 408. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 400 via the ROM 402 and/or the communication unit 409. When loaded into RAM 403 and executed by computing unit 401, may perform one or more steps of the methods described in the present disclosure. Alternatively, in other embodiments, the computing unit 401 may be configured by any other suitable means (e.g., by means of firmware) to perform the methods described by the present disclosure.

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.

The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so as to solve the defects of high management difficulty and weak service expansibility in the traditional physical host and Virtual Private Server (VPS). The server may also be a server of a distributed system, or a server incorporating a blockchain. Cloud computing refers to accessing an elastically extensible shared physical or virtual resource pool through a network, resources can include servers, operating systems, networks, software, applications, storage devices and the like, a technical system for deploying and managing the resources in a self-service mode as required can be achieved, and efficient and powerful data processing capacity can be provided for technical applications and model training of artificial intelligence, block chains and the like through a cloud computing technology.

It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, and the present disclosure is not limited herein.

The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.