Patent Publication Number: US-2022222491-A1

Title: System and method for lightweight semantic masking

Description:
CROSS-REFERENCE TO RELATED APPLICATION AND PRIORITY CLAIM 
     This application claims priority under 35 U.S.C. § 119(e) to U.S. Provisional Patent Application No. 63/136,619 filed on Jan. 12, 2021, which is hereby incorporated by reference in its entirety. 
    
    
     TECHNICAL FIELD 
     This disclosure relates generally to machine learning systems. More specifically, this disclosure relates to a system and method for lightweight semantic masking. 
     BACKGROUND 
     Data-driven natural language processing is prevalent in many commercialized services, such as voice-based searches and virtual assistants. Some conventional natural language processing techniques use a transformer-based language model for various language understanding tasks. However, these types of models often present various drawbacks in commercialization. Typically, these types of models are very large and require significant computing power in training and significant storage for parameters. These types of models can be especially impractical for on-device training and inferencing in Internet-of-Things (IoT) devices and mobile devices because of the limited resources and long training times in such devices. 
     SUMMARY 
     This disclosure provides a system and method for lightweight semantic masking. 
     In a first embodiment, a method includes performing, using at least one processor of an electronic device, semantic probing on a pre-trained model using one or more textual utterances. Performing the semantic probing includes processing each of the one or more textual utterances to determine a performance score for one or more targeted hidden layers of the pre-trained model. Performing the semantic probing also includes selecting a subset of the targeted hidden layers based on a comparison of the performance score to a predetermined threshold. The method also includes reconstructing, using the at least one processor, the pre-trained model based on the semantic probing to generate a reconstructed model. 
     In a second embodiment, an electronic device includes at least one memory configured to store instructions. The electronic device also includes at least one processing device configured when executing the instructions to perform semantic probing on a pre-trained model using one or more textual utterances. To perform the semantic probing, the at least one processing device is configured when executing the instructions to process each of the one or more textual utterances to generate a performance score for one or more targeted hidden layers of the pre-trained model. To perform the semantic probing, the at least one processing device is also configured when executing the instructions to select a subset of the targeted hidden layers based on a comparison of the performance score to a predetermined threshold. The at least one processing device is also configured when executing the instructions to reconstruct the pre-trained model based on the semantic probing to generate a reconstructed model. 
     In a third embodiment, a non-transitory machine-readable medium contains instructions that when executed cause at least one processor of an electronic device to perform semantic probing on a pre-trained model using one or more textual utterances. The instructions that when executed cause the at least one processor to perform the semantic probing include instructions that when executed cause the at least one processor to process each of the one or more textual utterances to generate a performance score for one or more targeted hidden layers of the pre-trained model. The instructions that when executed cause the at least one processor to perform the semantic probing also include instructions that when executed cause the at least one processor to select a subset of the targeted hidden layers based on a comparison of the performance score to a predetermined threshold. The medium also contains instructions that when executed cause the at least one processor to reconstruct the pre-trained model based on the semantic probing to generate a reconstructed model. 
     Other technical features may be readily apparent to one skilled in the art from the following figures, descriptions, and claims. 
     Before undertaking the DETAILED DESCRIPTION below, it may be advantageous to set forth definitions of certain words and phrases used throughout this patent document. The terms “transmit,” “receive,” and “communicate,” as well as derivatives thereof, encompass both direct and indirect communication. The terms “include” and “comprise,” as well as derivatives thereof, mean inclusion without limitation. The term “or” is inclusive, meaning and/or. The phrase “associated with,” as well as derivatives thereof, means to include, be included within, interconnect with, contain, be contained within, connect to or with, couple to or with, be communicable with, cooperate with, interleave, juxtapose, be proximate to, be bound to or with, have, have a property of, have a relationship to or with, or the like. 
     Moreover, various functions described below can be implemented or supported by one or more computer programs, each of which is formed from computer readable program code and embodied in a computer readable medium. The terms “application” and “program” refer to one or more computer programs, software components, sets of instructions, procedures, functions, objects, classes, instances, related data, or a portion thereof adapted for implementation in a suitable computer readable program code. The phrase “computer readable program code” includes any type of computer code, including source code, object code, and executable code. The phrase “computer readable medium” includes any type of medium capable of being accessed by a computer, such as read only memory (ROM), random access memory (RAM), a hard disk drive, a compact disc (CD), a digital video disc (DVD), or any other type of memory. A “non-transitory” computer readable medium excludes wired, wireless, optical, or other communication links that transport transitory electrical or other signals. A non-transitory computer readable medium includes media where data can be permanently stored and media where data can be stored and later overwritten, such as a rewritable optical disc or an erasable memory device. 
     As used here, terms and phrases such as “have,” “may have,” “include,” or “may include” a feature (like a number, function, operation, or component such as a part) indicate the existence of the feature and do not exclude the existence of other features. Also, as used here, the phrases “A or B,” “at least one of A and/or B,” or “one or more of A and/or B” may include all possible combinations of A and B. For example, “A or B,” “at least one of A and B,” and “at least one of A or B” may indicate all of (1) including at least one A, (2) including at least one B, or (3) including at least one A and at least one B. Further, as used here, the terms “first” and “second” may modify various components regardless of importance and do not limit the components. These terms are only used to distinguish one component from another. For example, a first user device and a second user device may indicate different user devices from each other, regardless of the order or importance of the devices. A first component may be denoted a second component and vice versa without departing from the scope of this disclosure. 
     It will be understood that, when an element (such as a first element) is referred to as being (operatively or communicatively) “coupled with/to” or “connected with/to” another element (such as a second element), it can be coupled or connected with/to the other element directly or via a third element. In contrast, it will be understood that, when an element (such as a first element) is referred to as being “directly coupled with/to” or “directly connected with/to” another element (such as a second element), no other element (such as a third element) intervenes between the element and the other element. 
     As used here, the phrase “configured (or set) to” may be interchangeably used with the phrases “suitable for,” “having the capacity to,” “designed to,” “adapted to,” “made to,” or “capable of” depending on the circumstances. The phrase “configured (or set) to” does not essentially mean “specifically designed in hardware to.” Rather, the phrase “configured to” may mean that a device can perform an operation together with another device or parts. For example, the phrase “processor configured (or set) to perform A, B, and C” may mean a generic-purpose processor (such as a CPU or application processor) that may perform the operations by executing one or more software programs stored in a memory device or a dedicated processor (such as an embedded processor) for performing the operations. 
     The terms and phrases as used here are provided merely to describe some embodiments of this disclosure but not to limit the scope of other embodiments of this disclosure. It is to be understood that the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise. All terms and phrases, including technical and scientific terms and phrases, used here have the same meanings as commonly understood by one of ordinary skill in the art to which the embodiments of this disclosure belong. It will be further understood that terms and phrases, such as those defined in commonly-used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined here. In some cases, the terms and phrases defined here may be interpreted to exclude embodiments of this disclosure. 
     Examples of an “electronic device” according to embodiments of this disclosure may include at least one of a smartphone, a tablet personal computer (PC), a mobile phone, a video phone, an e-book reader, a desktop PC, a laptop computer, a netbook computer, a workstation, a personal digital assistant (PDA), a portable multimedia player (PMP), an MP3 player, a mobile medical device, a camera, or a wearable device (such as smart glasses, a head-mounted device (HMD), electronic clothes, an electronic bracelet, an electronic necklace, an electronic accessory, an electronic tattoo, a smart mirror, or a smart watch). Other examples of an electronic device include a smart home appliance. Examples of the smart home appliance may include at least one of a television, a digital video disc (DVD) player, an audio player, a refrigerator, an air conditioner, a cleaner, an oven, a microwave oven, a washer, a drier, an air cleaner, a set-top box, a home automation control panel, a security control panel, a TV box (such as SAMSUNG HOMESYNC, APPLETV, or GOOGLE TV), a smart speaker or speaker with an integrated digital assistant (such as SAMSUNG GALAXY HOME, APPLE HOMEPOD, or AMAZON ECHO), a gaming console (such as an XBOX, PLAYSTATION, or NINTENDO), an electronic dictionary, an electronic key, a camcorder, or an electronic picture frame. Still other examples of an electronic device include at least one of various medical devices (such as diverse portable medical measuring devices (like a blood sugar measuring device, a heartbeat measuring device, or a body temperature measuring device), a magnetic resource angiography (MRA) device, a magnetic resource imaging (MRI) device, a computed tomography (CT) device, an imaging device, or an ultrasonic device), a navigation device, a global positioning system (GPS) receiver, an event data recorder (EDR), a flight data recorder (FDR), an automotive infotainment device, a sailing electronic device (such as a sailing navigation device or a gyro compass), avionics, security devices, vehicular head units, industrial or home robots, automatic teller machines (ATMs), point of sales (POS) devices, or Internet of Things (IoT) devices (such as a bulb, various sensors, electric or gas meter, sprinkler, fire alarm, thermostat, street light, toaster, fitness equipment, hot water tank, heater, or boiler). Other examples of an electronic device include at least one part of a piece of furniture or building/structure, an electronic board, an electronic signature receiving device, a projector, or various measurement devices (such as devices for measuring water, electricity, gas, or electromagnetic waves). Note that, according to various embodiments of this disclosure, an electronic device may be one or a combination of the above-listed devices. According to some embodiments of this disclosure, the electronic device may be a flexible electronic device. The electronic device disclosed here is not limited to the above-listed devices and may include new electronic devices depending on the development of technology. 
     In the following description, electronic devices are described with reference to the accompanying drawings, according to various embodiments of this disclosure. As used here, the term “user” may denote a human or another device (such as an artificial intelligent electronic device) using the electronic device. 
     Definitions for other certain words and phrases may be provided throughout this patent document. Those of ordinary skill in the art should understand that in many if not most instances, such definitions apply to prior as well as future uses of such defined words and phrases. 
     None of the description in this application should be read as implying that any particular element, step, or function is an essential element that must be included in the claim scope. The scope of patented subject matter is defined only by the claims. Moreover, none of the claims is intended to invoke 35 U.S.C. § 112(f) unless the exact words “means for” are followed by a participle. Use of any other term, including without limitation “mechanism,” “module,” “device,” “unit,” “component,” “element,” “member,” “apparatus,” “machine,” “system,” “processor,” or “controller,” within a claim is understood by the Applicant to refer to structures known to those skilled in the relevant art and is not intended to invoke 35 U.S.C. § 112(f). 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       For a more complete understanding of this disclosure and its advantages, reference is now made to the following description taken in conjunction with the accompanying drawings, in which like reference numerals represent like parts: 
         FIG. 1  illustrates an example network configuration including an electronic device according to this disclosure; 
         FIG. 2  illustrates an example process for lightweight semantic masking according to this disclosure; 
         FIGS. 3A and 3B  illustrate an example semantic probing process for use in the lightweight semantic masking process of  FIG. 2  according to this disclosure; 
         FIG. 4  illustrates an example binary masking process for use in the lightweight semantic masking process of  FIG. 2  according to this disclosure; and 
         FIG. 5  illustrates an example method for lightweight semantic masking according to this disclosure. 
     
    
    
     DETAILED DESCRIPTION 
       FIGS. 1 through 5 , discussed below, and the various embodiments of this disclosure are described with reference to the accompanying drawings. However, it should be appreciated that this disclosure is not limited to these embodiments and all changes and/or equivalents or replacements thereto also belong to the scope of this disclosure. 
     As noted above, data-driven natural language processing is prevalent in many commercialized services, such as voice-based searches and virtual assistants. Some conventional natural language processing techniques use a transformer-based language model (such as Bidirectional Encoder Representations from Transformers or “BERT”) for various language understanding tasks. Some of these models are pre-trained with multi-task objectives, such as masked language modeling and next-sentence prediction, and may use billions of English words. However, these types of models often present various drawbacks in commercialization. Typically, these types of models are very large and require significant computing power in training and significant storage for parameters. These types of models can be especially impractical for on-device training and inferencing in Internet-of-Things (IoT) devices and mobile devices because of the limited resources and long training times in such devices. For example, even simple task training with small amounts of data can take hours because the training may update a very large number of parameters (such as millions, hundreds of millions, or even more parameters) of a pre-trained model with fine-tuning. 
     This disclosure provides systems and methods for lightweight semantic masking, which perform semantic probing on a pre-trained model using textual utterances. The semantic probing is performed to reduce the size of the model and reduce training time while still achieving most of the performance of conventional training. The disclosed systems and methods also generate a parameter-reducing binary mask (such as a 2-bit representation instead of a 32-bit floating-point representation) for a task using a reconstructed model based on the pre-trained model. This further reduces the size of the model and substantially reduces the time needed for training. Applying the binary mask enables efficient and effective usage of space and processing power for handling multiple language understanding tasks. The resulting model can be significantly smaller (such as up to 90% smaller or more) while still maintaining performance that is equal to, or nearly equal to, the performance of a much larger model. The resulting model can be incorporated into a variety of devices, such as consumer electronic devices like smartphones, smart watches, refrigerators, washers, dryers, cleaning robots, and the like. Note that while some of the embodiments discussed below are described in the context of neural networks, this is merely one example, and it will be understood that the principles of this disclosure may be implemented in any number of other suitable contexts. 
       FIG. 1  illustrates an example network configuration  100  including an electronic device according to this disclosure. The embodiment of the network configuration  100  shown in  FIG. 1  is for illustration only. Other embodiments of the network configuration  100  could be used without departing from the scope of this disclosure. 
     According to embodiments of this disclosure, an electronic device  101  is included in the network configuration  100 . The electronic device  101  can include at least one of a bus  110 , a processor  120 , a memory  130 , an input/output (I/O) interface  150 , a display  160 , a communication interface  170 , or a sensor  180 . In some embodiments, the electronic device  101  may exclude at least one of these components or may add at least one other component. The bus  110  includes a circuit for connecting the components  120 - 180  with one another and for transferring communications (such as control messages and/or data) between the components. 
     The processor  120  includes one or more of a central processing unit (CPU), an application processor (AP), or a communication processor (CP). The processor  120  is able to perform control on at least one of the other components of the electronic device  101  and/or perform an operation or data processing relating to communication. In some embodiments, the processor  120  can be a graphics processor unit (GPU). As described in more detail below, the processor  120  may perform one or more operations for lightweight semantic masking. 
     The memory  130  can include a volatile and/or non-volatile memory. For example, the memory  130  can store commands or data related to at least one other component of the electronic device  101 . According to embodiments of this disclosure, the memory  130  can store software and/or a program  140 . The program  140  includes, for example, a kernel  141 , middleware  143 , an application programming interface (API)  145 , and/or an application program (or “application”)  147 . At least a portion of the kernel  141 , middleware  143 , or API  145  may be denoted an operating system (OS). 
     The kernel  141  can control or manage system resources (such as the bus  110 , processor  120 , or memory  130 ) used to perform operations or functions implemented in other programs (such as the middleware  143 , API  145 , or application  147 ). The kernel  141  provides an interface that allows the middleware  143 , the API  145 , or the application  147  to access the individual components of the electronic device  101  to control or manage the system resources. The application  147  may support one or more functions for lightweight semantic masking as discussed below. These functions can be performed by a single application or by multiple applications that each carry out one or more of these functions. The middleware  143  can function as a relay to allow the API  145  or the application  147  to communicate data with the kernel  141 , for instance. A plurality of applications  147  can be provided. The middleware  143  is able to control work requests received from the applications  147 , such as by allocating the priority of using the system resources of the electronic device  101  (like the bus  110 , the processor  120 , or the memory  130 ) to at least one of the plurality of applications  147 . The API  145  is an interface allowing the application  147  to control functions provided from the kernel  141  or the middleware  143 . For example, the API  145  includes at least one interface or function (such as a command) for filing control, window control, image processing, or text control. 
     The I/O interface  150  serves as an interface that can, for example, transfer commands or data input from a user or other external devices to other component(s) of the electronic device  101 . The I/O interface  150  can also output commands or data received from other component(s) of the electronic device  101  to the user or the other external device. 
     The display  160  includes, for example, a liquid crystal display (LCD), a light emitting diode (LED) display, an organic light emitting diode (OLED) display, a quantum-dot light emitting diode (QLED) display, a microelectromechanical systems (MEMS) display, or an electronic paper display. The display  160  can also be a depth-aware display, such as a multi-focal display. The display  160  is able to display, for example, various contents (such as text, images, videos, icons, or symbols) to the user. The display  160  can include a touchscreen and may receive, for example, a touch, gesture, proximity, or hovering input using an electronic pen or a body portion of the user. 
     The communication interface  170 , for example, is able to set up communication between the electronic device  101  and an external electronic device (such as a first electronic device  102 , a second electronic device  104 , or a server  106 ). For example, the communication interface  170  can be connected with a network  162  or  164  through wireless or wired communication to communicate with the external electronic device. The communication interface  170  can be a wired or wireless transceiver or any other component for transmitting and receiving signals. 
     The wireless communication is able to use at least one of, for example, long term evolution (LTE), long term evolution-advanced (LTE-A), 5th generation wireless system (5G), millimeter-wave or 60 GHz wireless communication, Wireless USB, code division multiple access (CDMA), wideband code division multiple access (WCDMA), universal mobile telecommunication system (UMTS), wireless broadband (WiBro), or global system for mobile communication (GSM), as a cellular communication protocol. The wired connection can include, for example, at least one of a universal serial bus (USB), high definition multimedia interface (HDMI), recommended standard 232 (RS-232), or plain old telephone service (POTS). The network  162  or  164  includes at least one communication network, such as a computer network (like a local area network (LAN) or wide area network (WAN)), Internet, or a telephone network. 
     The electronic device  101  further includes one or more sensors  180  that can meter a physical quantity or detect an activation state of the electronic device  101  and convert metered or detected information into an electrical signal. For example, one or more sensors  180  can include one or more cameras or other imaging sensors for capturing images of scenes. The sensor(s)  180  can also include one or more buttons for touch input, a gesture sensor, a gyroscope or gyro sensor, an air pressure sensor, a magnetic sensor or magnetometer, an acceleration sensor or accelerometer, a grip sensor, a proximity sensor, a color sensor (such as a red green blue (RGB) sensor), a bio-physical sensor, a temperature sensor, a humidity sensor, an illumination sensor, an ultraviolet (UV) sensor, an electromyography (EMG) sensor, an electroencephalogram (EEG) sensor, an electrocardiogram (ECG) sensor, an infrared (IR) sensor, an ultrasound sensor, an iris sensor, or a fingerprint sensor. The sensor(s)  180  can further include an inertial measurement unit, which can include one or more accelerometers, gyroscopes, and other components. In addition, the sensor(s)  180  can include a control circuit for controlling at least one of the sensors included here. Any of these sensor(s)  180  can be located within the electronic device  101 . 
     The first external electronic device  102  or the second external electronic device  104  can be a wearable device or an electronic device-mountable wearable device (such as an HIVID). When the electronic device  101  is mounted in the electronic device  102  (such as the HIVID), the electronic device  101  can communicate with the electronic device  102  through the communication interface  170 . The electronic device  101  can be directly connected with the electronic device  102  to communicate with the electronic device  102  without involving with a separate network. The electronic device  101  can also be an augmented reality wearable device, such as eyeglasses, that include one or more cameras. 
     The first and second external electronic devices  102  and  104  and the server  106  each can be a device of the same or a different type from the electronic device  101 . According to certain embodiments of this disclosure, the server  106  includes a group of one or more servers. Also, according to certain embodiments of this disclosure, all or some of the operations executed on the electronic device  101  can be executed on another or multiple other electronic devices (such as the electronic devices  102  and  104  or server  106 ). Further, according to certain embodiments of this disclosure, when the electronic device  101  should perform some function or service automatically or at a request, the electronic device  101 , instead of executing the function or service on its own or additionally, can request another device (such as electronic devices  102  and  104  or server  106 ) to perform at least some functions associated therewith. The other electronic device (such as electronic devices  102  and  104  or server  106 ) is able to execute the requested functions or additional functions and transfer a result of the execution to the electronic device  101 . The electronic device  101  can provide a requested function or service by processing the received result as it is or additionally. To that end, a cloud computing, distributed computing, or client-server computing technique may be used, for example. While  FIG. 1  shows that the electronic device  101  includes the communication interface  170  to communicate with the external electronic device  104  or server  106  via the network  162  or  164 , the electronic device  101  may be independently operated without a separate communication function according to some embodiments of this disclosure. 
     The server  106  can include the same or similar components  110 - 180  as the electronic device  101  (or a suitable subset thereof). The server  106  can support to drive the electronic device  101  by performing at least one of operations (or functions) implemented on the electronic device  101 . For example, the server  106  can include a processing module or processor that may support the processor  120  implemented in the electronic device  101 . As described in more detail below, the server  106  may perform one or more operations to support lightweight semantic masking. 
     Although  FIG. 1  illustrates one example of a network configuration  100  including an electronic device  101 , various changes may be made to  FIG. 1 . For example, the network configuration  100  could include any number of each component in any suitable arrangement. In general, computing and communication systems come in a wide variety of configurations, and  FIG. 1  does not limit the scope of this disclosure to any particular configuration. Also, while  FIG. 1  illustrates one operational environment in which various features disclosed in this patent document can be used, these features could be used in any other suitable system. 
       FIG. 2  illustrates an example process  200  for lightweight semantic masking according to this disclosure. In some embodiments, the process  200  can be performed to reduce the number of parameters of a machine learning model used for training and inferencing, which is accomplished by use of semantic information and binary masking. For ease of explanation, the process  200  is described as being performed by the electronic device  101  shown in  FIG. 1 . However, the process  200  could be performed using any other suitable electronic device (such as the server  106  of  FIG. 1 ) and in any other suitable system. 
     As shown in  FIG. 2 , in the process  200 , the electronic device  101  receives a text input  205 . The text input  205  represents a textual sentence, question, or other text-based input for use in a classification task. An example text input  205  could be “Where is an Italian restaurant?” In some embodiments, the text input  205  is one of hundreds or thousands of inputs that may be used as training data. Note that, in some cases, the text-based input  205  may represent a spoken or other verbal or audio input that is converted into text. 
     The electronic device  101  performs one or more pre-processing operations  210  on the text input  205 . The pre-processing operations  210  represent any suitable text processing operations, such as tokenizing or removing unnecessary characters (like parentheses, semicolons, and the like) from the text input  205 . The pre-processing operations  210  modify the text input  205  to be suitable for use as an input to a pre-trained model  215 . 
     The pre-trained model  215  is a machine learning model used for natural language processing. In some embodiments, the pre-trained model  215  is a transformer-based contextual representation model. Also, in some embodiments, the pre-trained model  215  is a commercially-available model, such as a BERT model. In other embodiments, the pre-trained model  215  can be built or generated internally. For example, the pre-trained model  215  can be built using millions (or more) of utterances obtained from one or more users using a virtual assistant over time. The pre-trained model  215  can also be built using text obtained from different sources, such as WIKIPEDIA.COM or other online sources. 
     The pre-trained model  215  includes multiple layers  220 , which include multiple hidden layers like transformer layers. Each hidden layer is an encoder that outputs one or more vectors having a certain length. The hidden layers are connected in that the output of layer n (or a processed version of the output of layer n) is the input for layer n+1. Therefore, it can be determined whether a layer  220  is a hidden layer by checking its inputs and outputs. For example, in the BERT model, the inputs to an embedding layer (which is not a hidden layer) are tokens, and the outputs of the embedding layer are embeddings. Thus, if a particular layer  220  is identified as including tokens and/or embeddings, it can be determined that the layer  220  is not a hidden layer. In some embodiments, a list of hidden layers can be obtained from specification or reference information provided with the pre-trained model  215 . 
     The electronic device  101  performs a layer-by-layer examination of the layers  220  in the pre-trained model  215 . At operation  225 , the electronic device  101  determines whether or not a particular layer  220  is a hidden layer, such as by examining layer inputs and layer outputs (as discussed above), by reviewing specification or reference information provided with the pre-trained model  215 , or by using any other suitable technique for examining a layer of the pre-trained model  215 . If the electronic device  101  determines that a particular layer  220  is not a hidden layer, the electronic device  101  determines if there are any more layers  220  in the pre-trained model  215  to examine as shown at operation  240 . If the electronic device  101  determines that a particular layer  220  is a hidden layer, the electronic device  101  performs a semantic probing process  230  on the layer  220 . In the semantic probing process  230 , the electronic device  101  trains multiple contextual vectors, predicts semantic information, and determines whether or not to keep a particular layer  220  based on the layer&#39;s performance of semantic inference as discussed in greater detail below. 
       FIGS. 3A and 3B  illustrate an example semantic probing process  230  for use in the lightweight semantic masking process  200  of  FIG. 2  according to this disclosure. As shown in  FIGS. 3A and 3B , in the semantic probing process  230 , the electronic device  101  generates multiple semantic probing models  305  based on the pre-trained model  215 . In the example shown, the pre-trained model  215  includes twelve hidden layers  310 , which are a subset of the layers  220 . In some embodiments, the pre-trained model  215  is a transformer-based contextualized representation model, and the hidden layers  310  are transformer encoding layers. Of course, this is merely one example, and other pre-trained models  215  could include other numbers and types of hidden layers  310 . 
     In the semantic probing process  230 , the electronic device  101  targets the hidden layers  310  and generates an equal number (such as twelve) of small semantic probing models  305  to extract a set of contextual vectors  315  from the hidden layers  310 . For each semantic probing model  305 , the electronic device  101  builds one or more hidden layers  320 , one or more probing layers  325  (such as fully-connected layers), and one or more output activation layers  330  on top of the corresponding set of contextual vectors  315 . In some embodiments, the hidden layers  320  (such as multi-layer perceptron (MLP) layers) are optional and can be used to boost performance depending on resources and time constraints. The electronic device  101  obtains training data from a semantic database  335 , which contains textual utterances and semantic labels that are not part of the main training data (such as the text inputs  205 ) and are used for creating the semantic probing models  305 . For example, for a semantic label “Temperature,” a corresponding training utterance “Set the temperature to 69 degrees in the bedroom” can be used. This utterance may later be used for the classification task on the place, such as “room.” 
     The electronic device  101  trains each contextual vector  315  on the semantic probing model  305 , and the semantic probing model  305  predicts how much semantic information is present. As discussed below, the electronic device  101  determines whether or not to keep the hidden layer  310  based on the prediction performance of the semantic inference by the semantic probing model  305 . The contextualized representation of each hidden layer  310  differs in the semantic meaning and relationships between words as learned from co-occurrence statistics on unlabeled data. In the semantic probing process  230 , the electronic device  101  probes word-level contextual representations from the extracted contextual vectors  315 . 
     In predicting semantic information, the semantic probing model  305  measures how well semantic meanings can be extracted from the hidden layers  310  of the pre-trained model  215 . For example, the semantic probing model  305  can process the word-level contextual representations and determine how the contextual representations encode semantic information across words of sentences in long-range phenomena. The word-level contextual representations can be processed by encoding words using the hidden layers  310  of the pre-trained model  215  and reviewing the output of each hidden layer  310 . In some embodiments, the sematic meaning is labeled in the training data, and the performance can be measured as discussed below. Also, in some embodiments, the word-level contextual representations can be constructed by an attention mechanism in each hidden layer  310 . Of course, other suitable methods for constructing the word-level contextual representations are within the scope of this disclosure. 
     One objective of the semantic probing model  305  can include reducing or minimizing training time for training each hidden layer  310 . The training time and the size of the semantic probing model  305  can be reduced by using small training datasets (such as 300 training utterances and 60 validation utterances or other small datasets). Using a small training dataset, the semantic probing model  305  is trained by minimizing an objective function against the target semantic label. In some embodiments, the objective function is a binary cross-entropy loss function for a semantic label classification, and the training is ended by the accuracy saturated epoch. Of course, this is merely one example, and other objective functions can be used. 
     As discussed above, the semantic probing model  305  can train until the semantic accuracy performance is saturated. However, in some circumstances, the semantic probing model  305  can stop training early (such as before saturation). For example, the semantic probing model  305  may stop training early if there is a time limit, such as “training should finish in N seconds.” As another example, the semantic probing model  305  may train on only a subset of contextual vectors  315  (such as the first K contextual vectors  315 ) if there is limited memory. In general, there is a trade-off between performance and time or resources. That is, limiting the training time or number of trained contextual vectors  315  results in faster operation but tends to lower the performance of the semantic probing model  305 . 
     After prediction by the semantic probing model  305 , the electronic device  101  determines a prediction performance score  340  for the semantic probing model  305 . In some embodiments, the prediction performance score  340  is based on the accuracy of predictions or other suitable metric. Later, the electronic device  101  compares the prediction performance score  340  of the semantic probing model  305  to a threshold value β, which can be determined by heuristics, according to business requirements, or in any other suitable manner. If the prediction performance score  340  of the semantic probing model  305  is greater than the threshold value β, the hidden layer  215  corresponding to the semantic probing model  305  is selected. 
     Turning again to  FIG. 2 , the electronic device  101  determines if there are any more layers  220  in the pre-trained model  215  to examine at operation  240 . If there are additional layers, the electronic device  101  moves to the next layer  220  and performs the operation  225  and the semantic probing process  230  for that layer. For each hidden layer  310 , the electronic device  101  either selects or does not select the hidden layer  310  based on the results of the semantic probing process  230  for that layer. Following examination of all of the hidden layers  215 , a selected subset of the hidden layers  215  exists. Once there are no more layers  220  to examine, the electronic device  101  reconstructs the pre-trained model  215  to generates a reconstructed model  245 . 
     To generate the reconstructed model  245 , the electronic device  101  copies the pre-trained model  215  except for the hidden layers  310  that are re-processed by the semantic probing process  230 , and the electronic device  101  adds the selected subset of hidden layers  310  and corresponding parameters to the copied pre-trained model  215 . In some embodiments, the selected subset includes both one or more original hidden layers  310  and one or more updated hidden layers  310 . In some embodiments, it is possible that the semantic probing process  230  does not disregard any hidden layer  310  or modify any parameter. In that case, the reconstructed model  245  will be copied with all layers  220  of the pre-trained model  215 . After the electronic device  101  has generated the reconstructed model  245 , the electronic device  101  performs a binary masking process  250  on the reconstructed model  245  to determine a final semantic mask  255 . The binary masking process  250  is performed to select a subset of parameters that are important to one or more specific tasks of the reconstructed model  245  and discard unimportant parameters using binarization. 
       FIG. 4  illustrates an example binary masking process  250  for use in the lightweight semantic masking process  200  of  FIG. 2  according to this disclosure. In some embodiments, the binary masking process  250  may be performed for a specific task of interest. As shown in  FIG. 4 , with reference to the reconstructed model  245 , the electronic device  101  initializes multiple mask parameters  405  in a mask model  410  and associates the mask model  410  with the layers  220  of the reconstructed model  245 . The mask parameters  405  are initialized with real floating-point (non-binary) trainable numbers that are selected randomly, such as 32-bit floating point values. In some cases, the quantity of mask parameters  405  in the mask model  410  is the same as the quantity of parameters of the reconstructed model  245 . 
     The electronic device  101  performs training  415  of the mask model  410  for a specific task, such as a task with concatenated semantic and text embedding. For example, one natural language processing task (“entity recognition”) is a typical task for training the mask parameters  405 , and the task goal is to achieve the highest performance. The task is what the mask parameters  405  are optimized for and binarized at the end of the binary masking process  250 . After training for the specific task, the electronic device  101  determines the real number mask weights and produces a binary mask  420  in which the mask parameters  405  are updated with an element-wise parameter thresholding function K, such as K={1 if parameter value&gt;threshold value, 0 otherwise}. Here, the threshold value is a real number between 0 and 1 (such as 0.50). In some embodiments, the threshold value is determined heuristically. Thus, after the thresholding function, each mask parameter  405  has a value of 0 or 1. 
     Using the determined binary mask  420 , the electronic device  101  multiplies the parameters of the reconstructed model  245  by the mask parameters  405  in an element-wise binarization computation to generate masked parameters. Each masked parameter of the reconstructed model  245  is either retained or discarded. For example, if the value of the mask parameter  405  in the binary mask  420  is 0, the corresponding masked parameter of the reconstructed model  245  is discarded. If the value of the mask parameter  405  in the binary mask  420  is 1, the corresponding masked parameter of the reconstructed model  245  is retained for use during training and inferencing. Thus, the binarization is performed to filter out less meaningful parameters. 
     After binarization is completed, the electronic device  101  performs an evaluation operation  425  on the masked parameters to determine if the task goal has been achieved. In some embodiments, the task goal includes accuracy for classification, root mean square error for regression problems, and the like. If the task goal has not been achieved, the electronic device  101  performs another iteration of mask training  415 . In each iteration of the mask training  415 , the electronic device  101  acquires updated weights from the reconstructed model  245  using a product of the binary mask and the pretrained weights and updates the binary mask  420 . In some embodiments, the number of iterations performed can be up to the number of epochs for training. Also, in some embodiments, the electronic device  101  terminates training once accuracy saturation is reached. After the electronic device  101  concludes the iterations of mask training  415 , the electronic device  101  stores the finalized binary mask as the final semantic mask  255 , which (ideally) uses substantially less memory than a conventional model. For example, in some cases, the final semantic mask  255  may have only about 3% of the memory required for a conventional model that saves 32-bit floating point parameters. 
     Note that the operations and functions shown in  FIGS. 2 through 4  can be implemented in an electronic device  101 , server  106 , or other device in any suitable manner. For example, in some embodiments, these operations and functions can be implemented or supported using one or more software applications or other software instructions that are executed by the processor  120  of the electronic device  101 , server  106 , or other device. In other embodiments, at least some of these operations and functions can be implemented or supported using dedicated hardware components. In general, these operations and functions can be performed using any suitable hardware or any suitable combination of hardware and software/firmware instructions. 
     Although  FIGS. 2 through 4  illustrate one example of a process  200  for lightweight semantic masking and related details, various changes may be made to  FIGS. 2 through 4 . For example, while shown as a specific sequence of operations, various operations shown in  FIGS. 2 through 4  could overlap, occur in parallel, occur in a different order, or occur any number of times (including zero times). Also, the specific operations shown in  FIGS. 2 through 4  are examples only, and other techniques could be used to perform each of the operations shown in  FIGS. 2 through 4 . 
       FIG. 5  illustrates an example method  500  for lightweight semantic masking according to this disclosure. For ease of explanation, the method  500  shown in  FIG. 5  is described as involving the use of the process  200  shown in  FIGS. 2 through 4  and the electronic device  101  shown in  FIG. 1 . However, the method  500  shown in  FIG. 5  could be used with any other suitable electronic device (such as the server  106  of  FIG. 1 ) and in any other suitable system. 
     As shown in  FIG. 5 , semantic probing is performed on a pre-trained model using one or more textual utterances at step  502 . This could include, for example, the electronic device  101  performing the semantic probing process  230  on a pre-trained model  215  using one or more textual utterances from the semantic database  335 . The pre-trained model is reconstructed based on the semantic probing to generate a reconstructed model at step  504 . This could include, for example, the electronic device  101  reconstructing the pre-trained model  215  based on the semantic probing process  230  to generate a reconstructed model  245 . 
     A binary mask is generated based (at least in part) on the reconstructed model at step  506 . This could include, for example, the electronic device  101  generating and training a binary mask  420  using the binary masking process  250 , which is based (at least in part) on the reconstructed model  245 . The binary mask  420  can be stored and used as a final semantic mask  255 . In some embodiments, the binary mask can be generated for a specific task with semantic and text embedding. 
     Although  FIG. 5  illustrates one example of a method  500  for lightweight semantic masking, various changes may be made to  FIG. 5 . For example, while shown as a series of steps, various steps in  FIG. 5  could overlap, occur in parallel, occur in a different order, or occur any number of times. 
     Although this disclosure has been described with reference to various example embodiments, various changes and modifications may be suggested to one skilled in the art. It is intended that this disclosure encompass such changes and modifications as fall within the scope of the appended claims.