KR20220107737A - Method, system, and computer readable record medium for generating reformulated query - Google Patents

Abstract

A method, system, and computer readable recording medium for generating a restructured query are disclosed. The query generation method includes the steps of: generating first query candidates through query reformulation using a bidirectional encoder representations from transformer (BERT) based machine translation model for an original query; and selecting at least some of the first query candidates as a search query.

Description

Method, system, and computer-readable recording medium for generating a reconstructed query

The description below relates to a query reformulation (referred to as 'QR') technique for providing optimal search results.

A user may conduct a search to obtain desired information through a site such as a search engine. The user may obtain desired information by inputting a query into a query input window of a search engine through the user terminal and checking a search result output for the query.

In a query-term matching-based search system, a query reconstruction technique capable of providing an optimal search result by adding reconstructed queries having similar meanings to a user's query is used.

For example, in Korea Patent Application Laid-Open No. 10-2011-0007743 (published on January 25, 2011), a technique capable of correcting user queries determined to be misspelled queries based on statistical data according to the entire query unit or word unit This is disclosed.

A method and system for generating QR candidates for user queries are provided by additionally using the BERT (Bidirectional Encoder Representations from Transformer) model together with the SMT (statistical machine translation) model.

A method for generating a query executed on a computer device, the computer device comprising at least one processor configured to execute computer readable instructions contained in a memory, the method comprising: by the at least one processor: generating a first query candidate through query reformulation using a BERT (Bidirectional Encoder Representations from Transformer)-based machine translation model for a query; and selecting, by the at least one processor, at least a portion of the first query candidates as a search query.

According to an aspect, the generating may include generating the first query candidate through the BERT-based machine translation model, which is a machine translation model in which an encoder part of a transformer model is replaced with a BERT model.

According to another aspect, the method for generating a query further includes, by the at least one processor, storing a query candidate inferred using the BERT-based machine translation model in a cache, and the generating The doing may include finding and serving the first query candidate corresponding to the original query in the cache.

According to another aspect, the number of serving devices for processing a query in real time may be calculated based on at least one of a maximum number of inferences per unit time and a maximum unique query per second (UQPS).

According to another aspect, the generating includes the first through the BERT-based machine translation model trained by a pre-training technique using a masked language model (MLM) and next sentence prediction (NSP). You can create query candidates.

According to another aspect, the method for generating a query further includes, by the at least one processor, generating a second query candidate through query reconstruction using a statistical machine translation model with respect to the original query. may include

According to another aspect, the selecting may include selecting at least a portion of the first query candidate and the second query candidate as the search query based on a translation score.

According to another aspect, the selecting may include selecting at least a portion of the first query candidate and the second query candidate as the search query based on a translation score, and adding the second query candidate to the translation score of the first query candidate. A weight higher than that of the two query candidates may be applied.

According to another aspect, the selecting may include selecting at least a portion of the first query candidate and the second query candidate as the search query based on a translation score, and selecting the second query candidate from the first query candidate. The search query may be selected in a larger number or ratio.

According to another aspect, the selecting may include selecting at least a portion of the first query candidate and the second query candidate as the search query based on a translation score, and a search result corresponding to the first query candidate is The second query candidate may be displayed higher than a search result corresponding to the candidate.

There is provided a computer-readable recording medium in which a program for executing the query generating method in a computer is recorded.

A computer device comprising: at least one processor configured to execute computer readable instructions contained in a memory, wherein the at least one processor is configured to: perform query reconstruction using a BERT-based machine translation model for an original query to perform a first query There is provided a computer device comprising generating a candidate and selecting at least a part of the first query candidates as a search query.

According to embodiments of the present invention, QR quality and search quality can be improved by generating a QR candidate for a user query by further using the BERT model together with the SMT model.

1 is a diagram illustrating an example of a network environment according to an embodiment of the present invention.
2 is a block diagram illustrating an example of a computer device according to an embodiment of the present invention.
3 is a flowchart illustrating an example of a method that a computer device may perform according to an embodiment of the present invention.
4 shows an example of a multi-QR generation model for QR generation in an embodiment of the present invention.
5 to 9 show an example of an SMT model for QR generation in an embodiment of the present invention.
10 to 11 show an example of a BERT-based machine translation model for QR generation in an embodiment of the present invention.
12 shows an example of a BERT serving system for a real-time service according to an embodiment of the present invention.
13 shows an example of a cache-based BERT serving system architecture according to an embodiment of the present invention.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

The query generating system according to the embodiments of the present invention may be implemented by at least one computer device, and the query generating method according to the embodiments of the present invention is performed by at least one computer device included in the query generating system. can be In this case, the computer program according to an embodiment of the present invention may be installed and driven in the computer device, and the computer device may perform the query generating method according to the embodiments of the present invention under the control of the driven computer program. . The above-described computer program may be stored in a computer-readable recording medium in order to be combined with a computer device to cause the computer to execute the query generating method.

1 is a diagram illustrating an example of a network environment according to an embodiment of the present invention. The network environment of FIG. 1 shows an example including a plurality of electronic devices 110 , 120 , 130 , 140 , a plurality of servers 150 , 160 , and a network 170 . 1 is an example for explaining the invention, and the number of electronic devices or the number of servers is not limited as in FIG. 1 . In addition, the network environment of FIG. 1 only describes one example of environments applicable to the present embodiments, and the environment applicable to the present embodiments is not limited to the network environment of FIG. 1 .

The plurality of electronic devices 110 , 120 , 130 , and 140 may be a fixed terminal implemented as a computer device or a mobile terminal. Examples of the plurality of electronic devices 110 , 120 , 130 , 140 include a smart phone, a mobile phone, a navigation device, a computer, a notebook computer, a digital broadcasting terminal, a personal digital assistant (PDA), and a portable multimedia player (PMP). ), tablet PCs, etc. As an example, in FIG. 1 , the shape of a smartphone is shown as an example of the electronic device 110 , but in embodiments of the present invention, the electronic device 110 is substantially configured to be different through the network 170 using a wireless or wired communication method. It may refer to one of various physical computer devices capable of communicating with the electronic devices 120 , 130 , 140 and/or the servers 150 and 160 .

The communication method is not limited, and not only a communication method using a communication network (eg, a mobile communication network, a wired Internet, a wireless Internet, a broadcasting network) that the network 170 may include, but also short-range wireless communication between devices may be included. For example, the network 170 may include a personal area network (PAN), a local area network (LAN), a campus area network (CAN), a metropolitan area network (MAN), a wide area network (WAN), and a broadband network (BBN). , the Internet, and the like. In addition, the network 170 may include any one or more of a network topology including a bus network, a star network, a ring network, a mesh network, a star-bus network, a tree or a hierarchical network, etc. not limited

Each of the servers 150 and 160 communicates with the plurality of electronic devices 110 , 120 , 130 , 140 and the network 170 through a computer device or a plurality of computers providing commands, codes, files, contents, services, etc. It can be implemented in devices. For example, the server 150 may be a system that provides a target service (eg, a search service) to the plurality of electronic devices 110 , 120 , 130 , and 140 connected through the network 170 .

2 is a block diagram illustrating an example of a computer device according to an embodiment of the present invention. Each of the plurality of electronic devices 110 , 120 , 130 , 140 or the servers 150 and 160 described above may be implemented by the computer device 200 illustrated in FIG. 2 .

As shown in FIG. 2 , the computer device 200 may include a memory 210 , a processor 220 , a communication interface 230 , and an input/output interface 240 . The memory 210 is a computer-readable recording medium and may include a random access memory (RAM), a read only memory (ROM), and a permanent mass storage device such as a disk drive. Here, a non-volatile mass storage device such as a ROM and a disk drive may be included in the computer device 200 as a separate permanent storage device distinct from the memory 210 . Also, the memory 210 may store an operating system and at least one program code. These software components may be loaded into the memory 210 from a computer-readable recording medium separate from the memory 210 . The separate computer-readable recording medium may include a computer-readable recording medium such as a floppy drive, a disk, a tape, a DVD/CD-ROM drive, and a memory card. In another embodiment, the software components may be loaded into the memory 210 through the communication interface 230 rather than a computer-readable recording medium. For example, the software components may be loaded into the memory 210 of the computer device 200 based on a computer program installed by files received through the network 170 .

The processor 220 may be configured to process instructions of a computer program by performing basic arithmetic, logic, and input/output operations. The instructions may be provided to the processor 220 by the memory 210 or the communication interface 230 . For example, the processor 220 may be configured to execute a received instruction according to a program code stored in a recording device such as the memory 210 .

The communication interface 230 may provide a function for the computer device 200 to communicate with other devices (eg, the storage devices described above) through the network 170 . For example, a request, command, data, file, etc. generated by the processor 220 of the computer device 200 according to a program code stored in a recording device such as the memory 210 is transmitted to the network ( 170) to other devices. Conversely, signals, commands, data, files, etc. from other devices may be received by the computer device 200 through the communication interface 230 of the computer device 200 via the network 170 . A signal, command, or data received through the communication interface 230 may be transmitted to the processor 220 or the memory 210 , and the file may be a storage medium (described above) that the computer device 200 may further include. persistent storage).

The input/output interface 240 may be a means for an interface with the input/output device 250 . For example, the input device may include a device such as a microphone, keyboard, or mouse, and the output device may include a device such as a display or a speaker. As another example, the input/output interface 240 may be a means for an interface with a device in which functions for input and output are integrated into one, such as a touch screen. The input/output device 250 may be configured as one device with the computer device 200 .

Also, in other embodiments, the computer device 200 may include fewer or more components than those of FIG. 2 . However, there is no need to clearly show most of the prior art components. For example, the computer device 200 may be implemented to include at least a portion of the above-described input/output device 250 or may further include other components such as a transceiver and a database.

Hereinafter, specific embodiments of a method and system for generating a reconstructed query will be described.

The present embodiments relate to a QR technology for application to a query-term matching based search service. The goal is to view QR as a translation problem and transform it into a query suitable for search using a machine translation model. In particular, in this embodiment, a reconstructed query (hereinafter, referred to as a 'QR candidate') may be generated for a user query through a translation model in which the SMT model and the BERT model are combined.

3 is a flowchart illustrating an example of a method that a computer device may perform according to an embodiment of the present invention.

The computer device 200 according to the present embodiment may provide a search service to the client through a dedicated application installed on the client or a web/mobile site connection related to the computer device 200 . A computer-implemented query generating system may be configured in the computer device 200 . For example, the query generating system may be implemented in the form of a program that operates independently, or may be implemented in the form of an in-app of a specific application to be able to operate on the specific application.

The processor 220 of the computer device 200 may include at least one component as a component for performing the method for generating a query according to FIG. 3 . Depending on the embodiment, components of the processor 220 may be selectively included or excluded from the processor 220 . Also, according to an embodiment, the components of the processor 220 may be separated or merged to express the functions of the processor 220 .

The processor 220 and components of the processor 220 may control the computer device 200 to perform steps S310 to S330 included in the query generating method of FIG. 3 . For example, the processor 220 and components of the processor 220 may be implemented to execute instructions according to the code of the operating system included in the memory 210 and the code of at least one program.

Here, the components of the processor 220 may be expressions of different functions performed by the processor 220 according to instructions provided by the program code stored in the computer device 200 .

The processor 220 may read a necessary command from the memory 210 in which the command related to the control of the computer device 200 is loaded. In this case, the read command may include a command for controlling the processor 220 to execute steps S310 to S330 to be described later.

Steps S310 to S330 to be described later may be performed in an order different from the order shown in FIG. 3 , and some of the steps S310 to S330 may be omitted or additional processes may be further included.

Referring to FIG. 3 , in step S310 , the processor 220 may generate a first QR candidate group by reconstructing a user query using an SMT model that is a machine translation model. The processor 220 may generate the first QR candidate group through the SMT model trained with the parallel corpus training data.

In step S320, the processor 220 may generate the second QR candidate group by reconstructing the user query using the BERT-based machine translation model. While the SMT model can generate many QR candidates with a fast translation speed, it has a drawback in that it lacks context-aware translation. In order to compensate for this, the BERT model is applied to the QR. In the case of the BERT model, which is a classification model, unlike SMT, it is possible to understand the meaning of words and to translate them naturally reflecting the context. The BERT model can be trained with a pre-training technique using a masked language model (MLM) and next sentence prediction (NSP) for deeper language understanding.

In step S330, the processor 220 searches at least a part of the first QR candidate group and the second QR candidate group based on the translation score through the SMT model and the BERT-based machine translation model to provide a search result corresponding to the user's query. You can select by query. For example, the processor 220 may use a predetermined number or a predetermined ratio of QR candidates with a high rank based on the translation score as an actual search query.

The processor 220 may apply a higher weight than the first QR candidate group to the translation score of the second QR candidate group in the process of selecting the search query. In some embodiments, when selecting a high-order number or a certain ratio of QR candidates as a search query based on the translation score, it is also possible to select a larger number or ratio of QR candidates from the second QR candidate group than the first QR candidate group. . In addition, in providing the search result, the processor 220 may also expose the search result corresponding to the second QR candidate group above the search result of the first QR candidate group.

As shown in FIG. 4 , the processor 220 may include an SMT model 410 and a BERT-based machine translation model 420 as a multi-model structure. The processor 220 may generate the first QR candidate group 401 through the reconstruction using the SMT model 410 for the user query 40, and also the first QR candidate group 401 through the reconstruction using the BERT-based machine translation model 420. 2 QR candidate groups 402 may be generated.

The structure of the SMT model 410 is shown in FIG. 5 .

Referring to FIG. 5 , the SMT model 410 provides a translation result with a statistical probability, and includes a translation model 501 and a language model 502 .

The translation model 501 converts a source language into a target language, and requires parallel corpus training data in which a source sentence and a target sentence are paired.

The translation model 501 includes an alignment model and a lexical translation model, and as shown in FIG. 6 , the alignment 601 can be extracted.

The language model 502 predicts the probability of a sentence, and requires learning data for a target language. Referring to FIG. 7 , when consecutive words 'where are we going' are given, the probability that 'are' comes after 'where', the probability that 'we' comes after 'where are', etc. are calculated.

The SMT model 410 may create a statistical translation result by combining the translation model 501 and the language model 502 .

The QR system to which the SMT model 410 is applied is shown in FIG. 8 .

A translation model 501 and a language model 502 composed of an alignment model and a lexical translation model can be learned from the parallel corpus dictionary.

Referring to FIG. 8 , the QR system to which the SMT model 410 is applied goes through a tokenization process (morphological tokenization), a decoding process (SMT decoder), a result generation process (N best QR result), and a heuristic logic. By converting a source query, which is an original query, into a target query, a reconstructed query, that is, the first QR candidate group 401 may be generated.

For example, as shown in FIG. 9 , in the QR system to which the SMT model 410 is applied, when 'Naver free font' is given to the user query 901 that is the original query, 'Naver', 'free', 'face' After tokenization as shown above, 'Naver free font', 'Naver free font', and 'Naver free font' can be generated as a QR candidate group 902 for the user query 901 through the decoding process and result generation process. .

The QR system to which the SMT model 410 is applied has a limitation in that it depends on the language model 502 for the context, so there is a disadvantage in that the translation considering the context compared to deep learning is insufficient.

In this embodiment, a QR system using the BERT-based machine translation model 420 may be added to improve the quality limit of the SMT model 410 .

10 shows the BERT model structure.

Referring to FIG. 10, the BERT model 1000 receives a given sentence as an input, first embeds it in token units through an input embedding layer 1001, and then passes through a transformer layer 1002 to a token. It has a structure that expresses contextual representation with encoding considering the location information of

The transformer layer 1002 consists of a self-attention model instead of a model such as CNN or RNN, and the BERT model 1000 embeds a language using an encoder among the encoders and decoders of the transformer.

By replacing the encoder part of the transformer model with the BERT model 1000, it is possible to build a BERT-based machine translation model 420 for application to the QR system.

The QR system to which the BERT-based machine translation model 420 is applied is shown in FIG. 11 .

The BERT model, a classification model, shows very high performance when applied to various language processing or information retrieval fields. A QR system for generating a QR candidate through a translation model may be composed of an encoder 1110 for understanding an input query as a transformer model and a decoder 1120 for generating a QR candidate as a transformer model, as shown in FIG. The encoder 1110 part may be replaced with the BERT model 1000 .

The encoder 1110 can deeply understand the user query 1101, which is the original query, using the BERT model 1000, and understand the meaning of words to enable natural translation reflecting the context. The decoder 1120 may generate the QR candidate group 1102 for the user query 1101 as a decoder of the transformer model.

For example, as shown in FIG. 11 , the QR system to which the BERT-based machine translation model 420 is applied is deeper using the BERT model 1000 when 'Teenage women's music ranking' is given as the user query 1101. 'Teenage female music ranking', 'teenage female song ranking', 'teenage female music ranking', and 'teenage female music ranking' are generated as a QR candidate group 1102 for the user query 1101 through language understanding. can do.

Accordingly, the processor 220 may generate a QR candidate for providing an optimal search result by using the BERT-based machine translation model 420 together with the SMT model 410 as a multi-QR generation model.

In the case of the BERT-based machine translation model 420, it is difficult to guarantee performance in a real-time service because the model size is large.

Referring to FIG. 12 , the processor 220 may further include a cache-based BERT serving system 1230 to overcome the response speed problem of the BERT-based machine translation model 420 . The BERT serving system 1230 may apply a method of storing the inferred QR result of the BERT-based machine translation model 420 in a cache.

In general, since QR is performed prior to searching for each collection when a query is received in the integrated search, the response speed of the integrated search becomes slower as the response speed of the QR becomes slower. To solve this, a method of storing and using the inferred QR result of the BERT-based machine translation model 420 in the cache server can be applied by adding a cache server.

13 shows an example of a BERT serving system structure in an embodiment of the present invention.

Referring to FIG. 13 , the BERT serving system 1230 may include a cache server 1301 , a Neural Machine Translation (NMT) producer 1302 , a queue 1303 , and an NMT consumer 1304 .

The NMT producer 1302 serves to find a QR candidate corresponding to the user query by referring to the cache server 1301 when a user query is input and to return the result.

Meanwhile, the NMT producer 1302 inserts the user query into the queue 1303 when there is no QR result corresponding to the user query in the cache server 1301 . When the same query is continuously input, a blank value is inserted into the cache server 1301 because duplicate queries may be entered in the queue 1303 . The empty value inserted into the cache server 1301 may be temporarily maintained for a certain period of time (eg, 1 hour).

The queue 1303 serves as a message queue, such as Kafka, and may maintain a query received from the NMT producer 1302 and transmit it to the NMT consumer 1304 .

The NMT consumer 1304 may receive a query list from the queue 1303 and perform inference on the QR candidate using the BERT-based machine translation model 420 . The NMT consumer 1304 inserts the inference result for the QR candidate of the BERT-based machine translation model 420 into the cache server 1301 . The QR result inferred on the cache server 1301 may be updated in units of a predetermined time (eg, 1 day).

The processor 220 may calculate the number of serving equipment required to process the queries accumulated in real time. The processor 220 may determine the number of devices constituting the BERT serving system 1230 to process queries input in real time within a unit time (eg, 1 second).

As an example, the processor 220 may calculate the number of serving devices based on the maximum number of inferences per unit time and the maximum unique query per second (UQPS). It is more advantageous to process n queries in batches than to perform inference on one query n times. For example, to process UQPS 1,500 with a throughput of 1 second (16 queries), approximately 94 (1500/16=93.75) model server containers are required.

Therefore, in this embodiment, a real-time service can be guaranteed by using a cache-based serving platform to apply the BERT model, which has a large model size and slow response speed, to the QR system.

In addition to using a cache-based serving platform, it is also possible to reduce the parameters of the BERT model by three layers and apply a knowledge distillation technique for model compression to optimize performance.

As described above, according to embodiments of the present invention, QR quality and search quality can be improved by generating a QR candidate for a user query by additionally using the BERT model together with the SMT model.

The device described above may be implemented as a hardware component, a software component, and/or a combination of the hardware component and the software component. For example, the devices and components described in the embodiments may include a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), and a programmable logic unit (PLU). It may be implemented using one or more general purpose or special purpose computers, such as a logic unit, microprocessor, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. The processing device may also access, store, manipulate, process, and generate data in response to execution of the software. For convenience of understanding, although one processing device is sometimes described as being used, one of ordinary skill in the art will recognize that the processing device includes a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that may include For example, the processing device may include a plurality of processors or one processor and one controller. Other processing configurations are also possible, such as parallel processors.

The software may comprise a computer program, code, instructions, or a combination of one or more thereof, which configures a processing device to operate as desired or is independently or collectively processed You can command the device. The software and/or data may be embodied in any type of machine, component, physical device, computer storage medium or device for interpretation by or providing instructions or data to the processing device. have. The software may be distributed over networked computer systems and stored or executed in a distributed manner. Software and data may be stored in one or more computer-readable recording media.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded in a computer-readable medium. In this case, the medium may be to continuously store the program executable by the computer, or to temporarily store the program for execution or download. In addition, the medium may be various recording means or storage means in the form of a single or several hardware combined, it is not limited to a medium directly connected to any computer system, and may exist distributed on a network. Examples of the medium include a hard disk, a magnetic medium such as a floppy disk and a magnetic tape, an optical recording medium such as CD-ROM and DVD, a magneto-optical medium such as a floppy disk, and those configured to store program instructions, including ROM, RAM, flash memory, and the like. In addition, examples of other media may include recording media or storage media managed by an app store that distributes applications, sites that supply or distribute other various software, and servers.

As described above, although the embodiments have been described with reference to the limited embodiments and drawings, various modifications and variations are possible by those skilled in the art from the above description. For example, the described techniques are performed in a different order than the described method, and/or the described components of the system, structure, apparatus, circuit, etc. are combined or combined in a different form than the described method, or other components Or substituted or substituted by equivalents may achieve an appropriate result.

Therefore, other implementations, other embodiments, and equivalents to the claims are also within the scope of the following claims.

Claims (20)

A method for generating a query executed on a computer device, the method comprising:
the computer device comprises at least one processor configured to execute computer readable instructions contained in a memory;
The query creation method is:
generating, by the at least one processor, a first query candidate through query reformulation using a BERT (Bidirectional Encoder Representations from Transformer)-based machine translation model for an original query; and
selecting, by the at least one processor, at least a portion of the first query candidates as a search query;
How to create a query that includes. According to claim 1,
The generating step is
Generating the first query candidate through the BERT-based machine translation model, which is a machine translation model in which the encoder part of the transformer model is replaced with a BERT model
A method for generating a query, characterized in that According to claim 1,
The query creation method is:
Storing, by the at least one processor, in a cache the query candidate inferred using the BERT-based machine translation model;
further comprising,
The generating step is
Finding and serving the first query candidate corresponding to the original query in the cache
How to create a query that includes. 4. The method of claim 3,
Calculating the number of serving devices for processing queries in real time based on at least one of the maximum number of inferences per unit time and the maximum unique query per second (UQPS)
A method for generating a query, characterized in that According to claim 1,
The generating step is
Generating the first query candidate through the BERT-based machine translation model trained by a pre-training technique using a masked language model (MLM) and next sentence prediction (NSP)
A method for generating a query, characterized in that According to claim 1,
The query creation method is:
generating, by the at least one processor, a second query candidate through query reconstruction using a statistical machine translation model with respect to the original query;
A method for generating a query further comprising: 7. The method of claim 6,
The selecting step is
selecting at least a portion of the first query candidate and the second query candidate as the search query based on the translation score;
A method for generating a query, characterized in that 7. The method of claim 6,
The selecting step is
selecting at least a portion of the first query candidate and the second query candidate as the search query based on the translation score;
A weight higher than that of the second query candidate is applied to the translation score of the first query candidate.
A method for generating a query, characterized in that 7. The method of claim 6,
The selecting step is
selecting at least a portion of the first query candidate and the second query candidate as the search query based on the translation score;
Selecting the search queries in a greater number or ratio than the second query candidates in the first query candidates
A method for generating a query, characterized in that 7. The method of claim 6,
The selecting step is
selecting at least a portion of the first query candidate and the second query candidate as the search query based on the translation score;
a search result corresponding to the first query candidate is exposed higher than a search result corresponding to the second query candidate;
A method for generating a query, characterized in that A computer-readable recording medium having recorded therein a program for causing a computer to execute the method for generating a query according to any one of claims 1 to 10. In a computer device,
at least one processor configured to execute computer readable instructions contained in memory
including,
the at least one processor,
For the original query, the first query candidate is generated through query reconstruction using a BERT-based machine translation model,
selecting at least some of the first query candidates as a search query
A computer device characterized by a. 13. The method of claim 12,
the at least one processor,
Generating the first query candidate through the BERT-based machine translation model, which is a machine translation model in which the encoder part of the transformer model is replaced with a BERT model
A computer device characterized by a. 13. The method of claim 12,
the at least one processor,
Storing the query candidates inferred using the BERT-based machine translation model in the cache,
Finding and serving the first query candidate corresponding to the original query in the cache
A computer device characterized by a. 15. The method of claim 14,
Calculating the number of serving devices for processing queries in real time based on at least one of the maximum number of inferences per unit time and the maximum UQPS
A computer device characterized by a. 13. The method of claim 12,
the at least one processor,
Generating the first query candidate through the BERT-based machine translation model trained by a pre-training technique using MLM and NSP
A computer device characterized by a. 13. The method of claim 12,
the at least one processor,
Generating a second query candidate through query reconstruction using a statistical machine translation model with respect to the original query
A computer device characterized by a. 18. The method of claim 17,
the at least one processor,
selecting at least a portion of the first query candidate and the second query candidate as the search query based on the translation score;
A computer device characterized by a. 18. The method of claim 17,
the at least one processor,
selecting at least a portion of the first query candidate and the second query candidate as the search query based on the translation score;
A weight higher than that of the second query candidate is applied to the translation score of the first query candidate.
A computer device characterized by a. 18. The method of claim 17,
the at least one processor,
selecting at least a portion of the first query candidate and the second query candidate as the search query based on the translation score;
Selecting the search queries in a greater number or ratio than the second query candidates in the first query candidates
A computer device characterized by a.

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