Using backpropagation to train a dialog system

Techniques described herein use backpropagation to train one or more machine learning (ML) models of a dialog system. For instance, a method includes accessing seed data that includes training tuples, where each training tuple comprising a respective logical form. The method includes converting the logical form of a training tuple to a converted logical form, by applying to the logical form a text-to-speech (TTS) subsystem, an automatic speech recognition (ASR) subsystem, and a semantic parser of a dialog system. The method includes determining a training signal by using an objective function to compare the converted logical form to the logical form. The method further includes training the TTS subsystem, the ASR subsystem, and the semantic parser via backpropagation based on the training signal. As a result of the training by backpropagation, the machine learning models are tuned work effectively together within a pipeline of the dialog system.

TECHNICAL FIELD

The present disclosure relates to dialog systems and, more particularly, to techniques for using backpropagation to train machine learning models of a dialog system, for instance, where that training is based on actual predictions made by machine learning models in a workflow pipeline of the dialog system, such that the machine learning models learn to implicitly correct errors made within the dialog system.

BACKGROUND

An increasing number of devices now enable users to interact with the devices directly using voice or spoken speech. For example, a user can speak to such a device in a natural language, and in doing so, the user can ask a question or make a statement requesting an action to be performed. In response, the device performs the requested action or responds to the user's question using audio output. Since interacting directly using voice is a more natural and intuitive way for humans to communicate with their surroundings, the popularity of such speech-based systems is growing at an astronomical rate.

BRIEF SUMMARY

The present disclosure relates to techniques for using backpropagation (i.e., backward propagation of errors) to train one or more machine learning models of a dialog system. Specifically, such machine learning models, also referred to herein as models, may include one or more of a text-to-speech (TTS) subsystem, an automatic speech recognition (ASR) subsystem, and a semantic parser subsystem. As a result of such training, the models may be tuned work effectively within a pipeline of the dialog system.

A training system according to some embodiments utilizes seed data as a basis for training various models in the dialog system. In some embodiments, the seed data includes a set of tuples, each tuple including an original utterance and a corresponding original logical form that represents the original utterance. In some embodiments, the training system includes a conversion subsystem, which incorporates one or more models selected from the dialog system. The conversion subsystem performs a sequence of one, two, or more conversions. For each tuple in the seed data, the conversion subsystem of the training system converts the tuple to a converted tuple, and the training system may then compare the each converted tuple to the corresponding tuple from the seed data to determine how to update the machine learning models participating in the conversion subsystem so as to improve the accuracy of conversions.

Specifically, some embodiments of the conversion subsystem utilize, and thus include, a TTS subsystem, an ASR subsystem, and a semantic parser subsystem selected from a dialog system. The conversion subsystem may also utilize, and thus include, an inverse sequence-to-sequence (seq2seq) model that is the inverse of the semantic parser subsystem. For each tuple in the seed data, the conversion subsystem applies the inverse sequence-to-sequence (seq2seq) model to the original logical form of the tuple to cause the inverse seq2seq model to determine a second utterance. The conversion subsystem applies the TTS subsystem to the second utterance to cause the TTS subsystem to determine audio data. The conversion subsystem applies the ASR subsystem to the audio data to cause the ASR subsystem to determine a third utterance. The conversion subsystem applies the semantic parser subsystem to the third utterance to cause the semantic parser subsystem to determine a converted logical form.

In some embodiments, for each tuple of the training data, the training system applies an objective function to determine a degree of difference between the converted logical form and the original logical form from the tuple, which are ideally the same. The training system may use the result of the objective function to train the inverse seq2seq model, the TTS subsystem, the ASR subsystem, and the semantic parser subsystem by way of backpropagation. As a result, the TTS subsystem, the ASR subsystem, and the semantic parser subsystem may be tuned to work more effectively together within the dialog system.

DETAILED DESCRIPTION

A voice-enabled system that is capable of having a dialog with a user via speech inputs and audio outputs, also referred to as voice outputs, can come in various forms. For example, such a system may be provided as a stand-alone device, as a digital or virtual assistant, as a voice-capable service, or the like. In each of these forms, the system is capable of receiving speech inputs, understanding the speech inputs, generating responses or taking actions responsive to the speech inputs, and outputting the responses using audio outputs. In certain embodiments, the dialog functionality in such a voice-enabled system is provided by a dialog system or infrastructure (“dialog system”). The dialog system is configured to receive speech inputs, interpret the speech inputs, maintain a dialog, possibly perform or cause one or more actions to be performed based on interpretations of the speech inputs, prepare appropriate responses, and output the responses to the user using audio output.

Conventionally, a dialog system includes various machine learning (ML) models, such as an automatic speech recognition (ASR) subsystem, a semantic parser subsystem, and a text-to-speech (TTS) subsystem. These ML models are typically trained with clean data, i.e., data that is not the output of a different component of the dialog system. As a result, the ML models learn to handle clean data, rather than data that has already been processed and likely has had errors introduced. For instance, if the ASR subsystem makes an error in translating speech input to text, then that error is passed along to the semantic parser in the form of an inaccurate utterance. The semantic parser subsystem then produces a logical form based on the inaccurate utterance. Analogously, if the semantic parser subsystem makes an error, that error is passed along to the dialog manager subsystem, which generates response text as a reply to the original speech input based on a propagation of errors throughout the pipeline of the dialog system. The TTS subsystem then generates speech output based on the response text and, thus, indirectly based on one or more errors in the dialog system. Such errors can dramatically diminish the user experience when a user seeks a dialog with the dialog system.

Embodiments described herein provide improved techniques for training one or more ML models of the dialog system. In some embodiments, a training system described herein utilizes backpropagation (i.e., backward propagation of errors) to train such ML models. For instance, a training system described herein utilizes a set of seed data including various training tuples, in which each training tuple includes a respective utterance and a corresponding logical form. The training system uses one or more ML models of a dialog system to convert a training tuple of the seed data to one or more other formats, such that the ML models together determine a converted training tuple. The converted training tuple is thus a representation of the training tuple after the application of ML models of the dialog system. Ideally, because the ML models translate data from one format to another (e.g., from an utterance to a logical form representing the utterance), the converted training tuple should match the training tuple. For instance, if the ML models convert the logical form of the training tuple to an utterance, to audio data, to a second utterance, and then to a second logical form, the second logical form should ideally be the same as the logical form from the seed data. In some embodiments, the training system compares the converted training tuple to the training tuple, and the training system uses an error between the converted training tuple and the training tuple as a training signal with which to train the ML models used in the conversion via backpropagation.

Thus, through backpropagation, each ML model trained as described herein may be tuned to work with other ML models of the dialog system. The result is a dialog system with ML models that are tuned based on errors expected within the pipeline of the dialog system, so as to reduce such errors over the entire pipeline of the dialog system during operation.

FIG. 1is a diagram of an example of a dialog system100utilizing an ASR subsystem108, a semantic parser114, and a TTS subsystem120trained by way of backpropagation, according to certain embodiments described herein. The dialog system100is configured to receive speech inputs104, also referred to as voice inputs, from a user102. The dialog system100may then interpret the speech inputs104. The dialog system100may maintain a dialog with a user102and may possibly perform or cause one or more actions to be performed based upon interpretations of the speech inputs104. The dialog system100may prepare appropriate responses and may output the responses to the user using voice or speech output, also referred to as audio output. The dialog system100is a specialized computing system that may be used for processing large amounts of data potentially using a large number of computer processing cycles. The numbers of devices depicted inFIG. 1are provided for illustrative purposes. Different numbers of devices may be used. For example, while each device, server, and system inFIG. 1is shown as a single device, multiple devices may be used instead.

In certain embodiments, the processing performed by the dialog system100is implemented by a pipeline of components or subsystems, including a speech input component105; a wake-word detection (WD) subsystem106; an ASR subsystem108, also referred to as an ASR108; a natural language understanding (NLU) subsystem110, which includes a named entity recognizer (NER) subsystem112and a semantic parser subsystem114; a dialog manager (DM) subsystem116; a natural language generator (NLG) subsystem118; a TTS subsystem120; and a speech output component124. The subsystems listed above may be implemented only in software (e.g., using code, a program, or instructions executable by one or more processors or cores), in hardware, or in a combination of hardware and software. In certain implementations, one or more of the subsystems may be combined into a single subsystem. Additionally or alternatively, in some implementations, the functions described herein as performed by a particular subsystem may be implemented by multiple subsystems.

The speech input component105includes hardware and software configured to receive speech input104. In some instances, the speech input component105may be part of the dialog system100. In some other instances, the speech input component105may be separate from and be communicatively coupled to the dialog system100. The speech input component105may, for example, include a microphone coupled to software configured to digitize and transmit speech input104to the wake-word detection subsystem106.

The wake-word detection (WD) subsystem106is configured to listen for and monitor a stream of audio input for input corresponding to a special sound or word or set of words, referred to as a wake-word. Upon detecting the wake-word for the dialog system100, the WD subsystem106is configured to activate the ASR subsystem108. In certain implementations, a user may be provided the ability to activate or deactivate the WD subsystem106(e.g., by pushing a button) to cause the WD subsystem106to listen for or stop listening for the wake-word. When activated, or when operating in active mode, the WD subsystem106is configured to continuously receive an audio input stream and process the audio input stream to identify audio input, such as speech input104, corresponding to the wake-word. When audio input corresponding to the wake-word is detected, the WD subsystem106activates the ASR subsystem108.

As described above, the WD subsystem106activates the ASR subsystem108. In some implementations of the dialog system100, mechanisms other than wake-word detection may be used to trigger or activate the ASR subsystem108. For example, in some implementations, a push button on a device may be used to trigger the ASR subsystem108without needing a wake-word. In such implementations, the WD subsystem106need not be provided. When the push button is pressed or activated, the speech input104received after the button activation is provided to the ASR subsystem108for processing. Additionally or alternatively, in some implementations, the ASR subsystem108may be activated upon receiving an input to be processed.

The ASR subsystem108is configured to receive and monitor speech input104after a trigger or wake-up signal (e.g., a wake-up signal may be sent by the WD subsystem106upon the detection of the wake-word in the speech input104, or the wake-up signal may be received upon the activation of a button) and to convert the speech input104to text. As part of its processing, the ASR subsystem108performs speech-to-text conversion. The speech input104may be in a natural language form, and the ASR subsystem108is configured to generate the corresponding natural language text in the language of the speech input104. This corresponding natural language text is referred to herein as an utterance. For instance, the speech input104received by the ASR subsystem108may include one or more words, phrases, clauses, sentences, questions, or the like. The ASR subsystem108is configured to generate an utterance for each spoken clause and feed the utterances to the NLU subsystem110for further processing.

The NLU subsystem110receives utterances generated by the ASR subsystem108. The utterances received by the NLU subsystem110from the ASR subsystem108may include text utterances corresponding to spoken words, phrases, clauses, or the like. The NLU subsystem110translates each utterance, or a series of utterances, to a corresponding logical form.

In certain implementations, the NLU subsystem110includes a named entity recognizer (NER) subsystem112and a semantic parser subsystem114. The NER subsystem112receives an utterance as input, identifies named entities in the utterance, and tags the utterance with information related to the identified named entities. The tagged utterances are then fed to the semantic parser subsystem114, which is configured to generate a logical form for each tagged utterance, or for a series of tagged utterances. The logical form generated for an utterance may identify one or more intents corresponding to the utterance. An intent for an utterance identifies an objective of the utterance. Examples of intents include “order pizza” and “find directions.” An intent may, for example, identify an action that is requested to be performed. In addition to intents, a logical form generated for an utterance may also identify slots, also referred to as parameters or arguments, for an identified intent. For example, for the speech input “I'd like to order a large pepperoni pizza with mushrooms and olives,” the NLU subsystem110can identify the intent order pizza. The NLU subsystem can also identify and fill slots, e.g., pizza_size (filled with large) and pizza_toppings (filled with mushrooms and olives). The NLU subsystem110may use machine learning based techniques, rules, which may be domain specific, or a combination of machine learning techniques and rules to generate the logical forms. The logical forms generated by the NLU subsystem110are then fed to the DM subsystem116for further processing.

As shown inFIG. 1, in some embodiments, a training system150described herein trains one or more ML models of the dialog system100, such as the ASR subsystem108, the semantic parser subsystem114, and the TTS subsystem200. In some embodiments, as described in detail below, the training system150incorporates the one or more ML models into a conversion subsystem, which converts seed data into converted seed data. The training system150determines an error between the converted seed data and the seed data, and the training system150utilizes that error to train the one or more ML models via backpropagation. As a result, the one or more ML models are tuned to work together to reduce the propagation of errors through the dialog system100.

The DM subsystem116is configured to manage a dialog with the user based on logical forms received from the NLU subsystem110. As part of the dialog management, the DM subsystem116is configured to track dialog states, initiate the execution of or itself execute one of more actions or tasks, and determine how to interact with the user. These actions may include, for example, querying one or more databases, producing execution results, or other actions. For example, the DM subsystem116is configured to interpret the intents identified in the logical forms received from the NLU subsystem110. Based on the interpretations, the DM subsystem116may initiate one or more actions that it interprets as being requested by the speech inputs104provided by the user. In certain embodiments, the DM subsystem116performs dialog-state tracking based on current and past speech inputs104and based on a set of rules (e.g., dialog policies) configured for the DM subsystem116. These rules may specify the different dialog states, conditions for transitions between states, actions to be performed when in a particular state, or the like. These rules may be domain specific. The DM subsystem116also generates responses to be communicated back to the user involved in the dialog. These responses may be based upon actions initiated by the DM subsystem116and their results. The responses generated by the DM subsystem116are fed to the NLG subsystem118for further processing.

The NLG subsystem118is configured to generate natural language texts corresponding to the responses generated by the DM subsystem116. The texts may be generated in a form that enables them to be converted to speech by the TTS subsystem120. The TTS subsystem120receives the texts from the NLG subsystem118and converts each of them to speech or voice audio, which may then be output as audio to the user via an audio or speech output component124of the dialog system (e.g., a speaker, or communication channel coupled to an external speaker). In some instances, the speech output component124may be part of the dialog system100. In some other instances, the speech output component124may be separate from and communicatively coupled to the dialog system100.

As described above, the various subsystems of the dialog system100working in cooperation provide the functionality that enables the dialog system100to receive speech inputs104and to respond using speech outputs122and, thereby, to maintain a dialog with a user using natural language speech. The various subsystems described above may be implemented using a single computer system or using multiple computer systems working cooperatively. For example, for a device implementing the voice-enabled system, the subsystems of the dialog system100described above may be implemented entirely on the device with which the user interacts. In some other implementations, some components or subsystems of the dialog system100may be implemented on the device with which the user interacts, while other components may be implemented remotely from the device, possibly on some other computing devices, platforms, or servers.

FIG. 2is a diagram of an example of the training system150, which is configured to train one or more ML models of a dialog system100, according to certain embodiments described herein. In some embodiments, the training system150is implemented as a computing device or portion thereof, such as a server. The training system150may be implemented as hardware, software, or a combination of both. For instance, the training system150may be a specialized hardware device or program code, or a combination of both. For instance, the operations described herein as being performed by the training system150may be embodied in program code implementing the training system150, where such program code is executable by one or more processing units.

As described above, the dialog system100includes various ML models in its pipeline, or workflow. More specifically, these models may include an ASR108, a semantic parser subsystem114, and a TTS subsystem120, potentially in addition to others. As shown inFIG. 2, the training system150may be configured to train one or more of such ML models that are selected from the dialog system100and incorporated into a conversion subsystem240of the training system150.

As described above, various ML models of the dialog system100are configured to translate, or convert, data from one format to another. For instance, a user provides speech input104to the dialog system100, such as by speaking. In some embodiments of the dialog system100, the ASR108translates the speech input104into an utterance225, which the semantic parser subsystem114translates into a logical form235, which the dialog manager subsystem116processes to determine a response, which the TTS subsystem120translates into speech output122responsive to the speech input104. By the conversion subsystem240of the training system150, seed data210is translated one or more times using one or more of these ML models. The training system150may compare the result of such translations, as performed by the conversion subsystem240, to the original seed data210to train the ML models that participate in the conversion subsystem240.

As shown inFIG. 2, the training system150may have access to a set of seed data210, also referred to as training data, which may include a set of tuples. Each tuple in the seed data210may include an utterance225and a corresponding logical form235(i.e., an expression of the utterance in the language of logical forms). Generally, the logical form235in a given tuple may be a structured translation of the corresponding utterance225in that given tuple. The seed data210may be appropriate for training the semantic parser subsystem114; during operation of the dialog system100, the semantic parser subsystem114, also referred to as the semantic parser114, takes an utterance225as input and determines a logical form235. However, in some embodiments, the seed data210need not, but can, be used to directly train the semantic parser114.

The conversion subsystem240may include one or more of the ML models of the dialog system100, and the training system150may be enabled to train each of such ML models to enable those ML models to operate more effectively in the dialog system100. For instance, as described below in detail, the conversion subsystem240may include the ASR108, the semantic parser114, and the TTS subsystem120of a dialog system100, and in that case, the training system150may train each of the ASR108, the semantic parser114, and the TTS subsystem120as described herein.

In the conversion subsystem240, one or more ML models of a dialog system100translate the seed data210into a converted version of the seed data210, and the training system150utilizes an objective function250to compare the converted version to the original seed data210. More specifically, in some embodiments, the conversion subsystem240takes as input an original logical form235afrom the seed data210and generates a converted logical form235bby translating the original logical form235ato one or more different formats (e.g., an utterance225or speech) and then back to the a logical form235. The translations may be performed by one or more ML models of the dialog system100. As such, the converted logical form235brepresents the original logical form235apotentially with errors introduced through processing by the ML models in the dialog system100. The conversion subsystem240may operate on each original logical form235ain the seed data210, thus enabling the training system150to train the ML models in the conversion subsystem240based on the resulting converted logical forms235b.

Ideally, because the converted logical form235bis a translation, each converted logical form235bshould be the same as the corresponding original logical form235afrom the seed data210. However, this may not be the case due to the introduction of errors by the ML models in the conversion subsystem240. In some embodiments, the training system150utilizes a difference between the original logical form235aand the converted logical form235bto train the ML models to behave better in the context of the pipeline of the dialog system100. Specifically, the training system150may apply an objective function250(i.e., a loss function) to each converted logical form235band its corresponding original logical form235ato determine a training value. Together, a stream or set of training values, determined based on performing the above operations for the various original logical forms235afrom the seed data210, form a training signal. In some embodiments, the training system150utilizes the training signal to train the ML models participating in the conversion subsystem240.

FIG. 3is a flow diagram of a method300of using backpropagation to train one or more ML models of a dialog system100, according to certain embodiments. In some embodiments, prior to execution of this method300, each ML model to be used by the conversion subsystem240of the training system150may have been, but need not have been, trained individually for use in the dialog system100. For example, if the conversion subsystem240includes a TTS subsystem120, then the TTS subsystem120has been trained to map utterances225to audio data455, such as through the use of training data that includes utterances225and their corresponding audio data455; if the conversion subsystem240includes an ASR108, then the ASR108has been trained to map speech input104to utterances225, such as through the use of training data that includes audio data455(e.g., speech input104) and corresponding utterances225; and if the conversion subsystem240includes a semantic parser114, then the semantic parser114has been trained to map utterances225to logical forms, such as through the use of training data that includes utterances225and corresponding logical forms235. Alternatively, training via backpropagation as performed by the training system150described herein may be used in lieu of training each model individually.

The method300depicted inFIG. 3, as well as other methods described herein, may be implemented in software (e.g., as code, instructions, or programs) executed by one or more processing units (e.g., processors or processor cores), in hardware, or in combinations thereof. The software may be stored on a non-transitory storage medium, such as on a memory device. This method300is intended to be illustrative and non-limiting. AlthoughFIG. 3depicts various activities occurring in a particular sequence or order, this is not intended to be limiting. In certain embodiments, for instance, the activities may be performed in a different order, or one or more activities of the method300may be performed in parallel. In certain embodiments, the method300may be performed by the training system150.

As shown inFIG. 3, at block305, the training system150accesses seed data210for use in the training system150. The seed data210may include a set of tuples, each tuple including an original utterance225as well as an original logical form235acorresponding to the original utterance225. It will be understood that various techniques may be used to collect the seed data210, and such techniques may be manual, automatic, or a combination of both.

In some embodiments, the collection of the seed data210may be, at least in part, a manual process. For instance, the seed data210may be crowdsourced. In some embodiments, a team of one or more individuals manually writes a grammar to describe a structure of the logical form235. The team may generate a set of original logical forms235ato be included in the seed data210and may provide such original logical forms235ato one or more individuals in a crowd, asking the crowd to convert each original logical form235ainto a corresponding original utterance225. Further, the team may provide an intermediate form to represent each such original logical form235a, where the intermediate form is an abrupt or choppy variation of natural language that is relatively simply to produce by the team and relatively simple to understand by the crowd. The intermediate form may assist the crowd in conversion because use of the intermediate form means the crowd need not learn the language of logical forms235. Thus, to convert an original logical form into natural language (i.e., a corresponding original utterance225), the crowd may convert the corresponding intermediate form into natural language. It will be understood that multiple utterances225can equate to a common logical form235, and thus, the crowd may generate one or more original utterances225based on an original logical form235a, and each such original utterance225may be combined in a respective tuple with the original logical form235a.

In another embodiment, a set of original utterances225are provided, and one or more individuals determine a logical form235for each such original utterance225. However, it may provide more efficient to start with a logical form235because individuals are likely more familiar with natural language and may thus make faster work of generating utterances225in natural language as compared to generating logical forms235in a less familiar language.

Block310beings an iterative loop in which each tuple of the seed data210is considered in turn. The training system150may iterate over the tuples in the seed data210. With each iteration, one or more ML models of the dialog system100that are included in the conversion subsystem240may be further tuned to provide accurate output. As described above and described further below, for each tuple of the seed data210, an embodiment of the training system150utilizes the conversion subsystem240to translate the tuple to a converted tuple. For instance, this may include converting the utterance225of the tuple to a converted utterance225or converting the original logical form235aof the tuple to a converted logical form235b, as in the example ofFIG. 2. The training system150then compares the converted tuple to the original tuple to train the ML models in the conversion subsystem240. Specifically, at block310, the training system150selects from the seed data210a tuple that has not yet been considered so as to perform these activities.

At block315, the conversion subsystem240of the training system150applies the one or more ML models of the conversion subsystem240to the selected tuple, which was selected to block310, to translate the selected tuple to a converted tuple. As described above, each ML model used by the conversion subsystem240may perform a type of translation. Thus, ideally, the converted tuple is equal to the selected tuple.

At block320, the training system150compares the converted tuple, determined at block315, to the selected tuple, determined at block310. For instance, the training system150may apply an objective function250to perform this comparison. In some embodiments, utilizing the objective function250or another technique, the training system150determines a degree of error between the converted tuple and the selected tuple, where the selected tuple is the desired result of the conversions performed by the conversion subsystem240.

At block325, the training system150trains the ML models used by the conversion subsystem240, or a subset of these ML models, using backpropagation based on the result of the comparison performed at block320. For instance, for an ML model implemented as a neural network, the training system150updates the weights of the nodes of such ML model based on the degree of error determined above.

At decision block330, the training system150determines whether any tuples remain for consideration in the seed data210. If one or more tuples have not yet been considered, then the method300returns to block310to select another tuple. However, if all such tuples have been considered, then the method300ends at block335, with the ML models of the dialog system100having been trained and being useable in the dialog system100.

FIG. 4is a diagram of another example of a training system150configured to train one or more ML models of a dialog system100, according to certain embodiments. In some embodiments, the training system150is implemented as a computing device or portion thereof, such as a server. The training system150may be implemented as a specialized hardware device or as program code, or a combination of both. For instance, the operations described herein as being performed by the training system150may be embodied in program code implementing the training system150, where such program code is executable by one or more processing units.

As described above, the conversion subsystem240of the training system150includes one or more ML models selected from the dialog system100, thus enabling the training system150to train those ML models using a training signal based on comparing their output (i.e., a converted logical form235b) to expected output (i.e., an original logical form235afrom the seed data210). The conversion subsystem240applies each such ML model to tuples of seed data210, as described further below, to determine the training signal. Specifically, in the example ofFIG. 4, the conversion subsystem240includes the ASR108, the semantic parser114, and the TTS subsystem120of a dialog system100. In some embodiments, the ASR108, the semantic parser114, and the TTS subsystem120may be selected from a single dialog system100that utilizes this specific ASR108, semantic parser114, and TTS subsystem120. The training described herein performed by the training system150may be in addition to conventional training, in which each such ML model participating in the conversion subsystem240may be trained on an individual basis, or in lieu of conventional training.

As shown inFIG. 4, the training system150may have access to a set of seed data210, which may include a set of tuples, each tuple including an utterance225and a corresponding original logical form235a. Logical forms235, such as the original logical form235a, may be syntactical expressions complying with a predefined grammar that is parseable by a dialog manager subsystem116of the dialog system100. Thus, the original logical form235ain a tuple may be a structured translation of the corresponding utterance225in the tuple. The seed data210may be appropriate for training the semantic parser114, which, during operation of the dialog system100, takes an utterance225as input and determines a logical form235. However, in some embodiments, the seed data210need not, but can, be used to directly train the semantic parser114. As described below, the conversion subsystem240may apply the one or more ML models participating in the conversion subsystem240to tuples of the seed data210to convert those tuples in order to determine a training signal.

In some embodiments, as in this example, the conversion subsystem240of the training system150may apply an inverse seq2seq model410to the original logical form235aof each tuple in the seed data210to cause the inverse seq2seq model410to determine a second utterance225corresponding to the original logical form235a. The second utterance225may thus be a translation of the logical form235selected from the tuple of the seed data210. In some embodiments, the inverse seq2seq model410is trained in parallel with the semantic parser114, as described herein, to be the inverse of the semantic parser114, which may be a seq2seq model. For instance, the semantic parser114inputs utterances225and outputs logical forms235, whereas the inverse seq2seq model410inputs logical forms235and thus outputs utterances225. More specifically, as trained herein, when provided with a logical form235output by the semantic parser114based on a specific utterance225, the inverse seq2seq model410would output that same specific utterance225. The second utterance225may be a textual translation of the original logical form235abut may include errors based on the potential inaccuracy in the inverse seq2seq model410and, as such, based on the potential inaccuracy in the semantic parser114.

In some embodiments, the conversion subsystem240of the training system150applies the TTS subsystem120to the second utterance225, as determined by the inverse seq2seq model410, to cause the TTS subsystem120to determine audio data455corresponding to the second utterance225and thus to the original logical form235a. The audio data455may thus be an audio translation of the original logical form235a; however, the audio data455may incorporate errors introduced by the inverse seq2seq model410or the TTS subsystem120. These would be the same types of errors as would be introduced during operation of the dialog system100because the TTS subsystem120is part of the dialog system100and because the inverse seq2seq model410is a representation of the semantic parser114, which is also part of the dialog system100.

In some embodiments, the conversion subsystem of the training system150applies the ASR108to the audio data455to cause the ASR108determine a third utterance225, which corresponds to the audio data455, the second utterance225, and the original logical form235a. The third utterance225may thus be a textual translation of the original logical form235a; however, the third utterance225may incorporate errors introduced by the inverse seq2seq model410, the TTS subsystem120, or the ASR108. These would be the same types of errors as would be introduced during operation of the dialog system100because the TTS subsystem120and the ASR108are part of the dialog system100and because the inverse seq2seq model410is a representation of the semantic parser114, which is also part of the dialog system100.

In some embodiments, the conversion subsystem240of the training system150applies the semantic parser114to the third utterance225to cause the semantic parser114to determine the converted logical form235b. The converted logical form235bmay thus be a translation of the original logical form235a; however, the converted logical form235bmay incorporate errors introduced by the inverse seq2seq model410, the TTS subsystem120, the ASR108, or the semantic parser114. These would be the same types of errors as would be introduced during operation of the dialog system100because the TTS subsystem120, the ASR108, and the semantic parser114are part of the dialog system100and because the inverse seq2seq model410is a representation of the semantic parser114, which is part of the dialog system100.

Ideally, because each of the inverse seq2seq model410, the TTS subsystem120, the ASR108, and the semantic parser114is an ML model learning to translate data from one form into another form, a converted logical form235bshould be the same as the corresponding original logical form235aselected from the seed data210. However, this may not be the case due to the introduction of errors by the inverse seq2seq model410, the TTS subsystem120, the ASR108, and the semantic parser114. In some embodiments, the training system150utilizes a difference between the converted logical form235band the corresponding original logical form235ato train the TTS subsystem120, the ASR108, and the semantic parser114to behave better in the context of the pipeline of the dialog system100. In other words, the training system150may teach the TTS subsystem120, the ASR108, and the semantic parser114to generate a more accurate converted logical form235b, thus tuning the TTS subsystem120, the ASR108, and the semantic parser114to operate together with a reduction in errors.

To train the TTS subsystem120, the ASR108, and the semantic parser, the training system150may apply an objective function250(i.e., a loss function) to the converted logical form235band the corresponding original logical form235ato determine a training value. Together, a stream or set of training values determined based on the various original logical forms235ain the seed data210form a training signal. In some embodiments, the training system150utilizes the training signal to train one or more of (e.g., each of) the inverse seq2seq model410, the TTS subsystem120, the ASR108, and the semantic parser114. As a result, the TTS subsystem120, the ASR108, and the semantic parser114learn to operate within the pipeline of the dialog system100and may thus more effectively work together.

FIG. 5is a diagram of another example of a method500of using backpropagation to train one or more ML models of a dialog system100, according to certain embodiments. Specifically, in this example, the conversion subsystem240applies the ASR108, the semantic parser114, and the TTS subsystem120, and the training system150trains the ASR108, the semantic parser114, and the TTS subsystem120using backpropagation.

In some embodiments, prior to execution of this method500, each of these ML models may have been, but need not have been, trained individually. For instance, given the conversion subsystem240in the example ofFIG. 4, the inverse seq2seq model410has been trained individually to map logical forms235to utterances225, such as through the use of training data that includes logical forms235and their corresponding utterances225, where the logical forms235are used as training input and the corresponding utterances are the expected output for training; the TTS subsystem120has been trained to map utterances225to audio data455, such as through the use of training data that includes utterances225and their corresponding audio data455; the ASR108has been trained to map speech input104to utterances225, such as through the use of training data that includes audio data455(e.g., speech input104) and corresponding utterances225; and the semantic parser114has been trained to map utterances225to logical forms, such as through the use of the same training data used to train the inverse seq2seq model410but with the utterances225as training input and the logical forms235as the expected output for training. Alternatively, training via backpropagation as performed by the training system150described herein may be used in lieu of training each model individually.

The method500depicted inFIG. 5, as well as other methods described herein, may be implemented in software (e.g., as code, instructions, or programs) executed by one or more processing units (e.g., processors or processor cores), in hardware, or in combinations thereof. The software may be stored on a non-transitory storage medium, such as on a memory device. This method500is intended to be illustrative and non-limiting. AlthoughFIG. 5depicts various activities occurring in a particular sequence or order, this is not intended to be limiting. In certain embodiments, for instance, the activities may be performed in a different order, or one or more activities of the method500may be performed in parallel. In certain embodiments, the method500may be performed by the training system150.

As shown inFIG. 5, at block505, the training system150accesses seed data210for use in the training system150. The seed data210may include a set of tuples, each tuple including an original utterance225as well as an original logical form235acorresponding to the original utterance225. It will be understood that various techniques may be used to collect the seed data210, and such techniques may be manual, automatic, or a combination of both.

In some embodiments, the collection of the seed data210may be, at least in part, a manual process. For instance, the seed data210may be crowdsourced. In some embodiments, a team of one or more individuals manually writes a grammar to describe a structure of the logical form235. The team may generate a set of original logical forms235ato be included in the seed data210and may provide such original logical forms235ato one or more individuals in a crowd, asking the crowd to convert each original logical form235ainto a corresponding original utterance225. Further, the team may provide an intermediate form to represent each such original logical form235a, where the intermediate form is an abrupt or choppy variation of natural language that is relatively simply to produce by the team and relatively simple to understand by the crowd. The intermediate form may assist the crowd in conversion because use of the intermediate form means the crowd need not learn the language of logical forms235. Thus, to convert an original logical form into natural language (i.e., a corresponding original utterance225), the crowd may convert the corresponding intermediate form into natural language. It will be understood that multiple utterances225can equate to a common logical form235, and thus, the crowd may generate one or more original utterances225based on an original logical form235a, and each such original utterance225may be combined in a respective tuple with the original logical form235a.

In another embodiment, a set of original utterances225are provided, and one or more individuals determine a logical form235for each such original utterance225. However, it may provide more efficient to start with a logical form235because individuals are likely more familiar with natural language and may thus make faster work of generating utterances225in natural language as compared to generating logical forms235in a less familiar language.

Block510beings an iterative loop in which each tuple of the seed data210is considered in turn. The training system150may iterate over the tuples in the seed data210. With each iteration, one or more ML models of the dialog system100may be further tuned to provide accurate output. As described above and described further below, for each tuple of the seed data210, an embodiment of the training system150utilizes the conversion subsystem240to translate the tuple, specifically the original logical form235aof the tuple, to a converted tuple, specifically to a converted logical form235b. The training system150then compares the converted tuple to the original tuple to train the ML models in the conversion subsystem240. Specifically, at block510, the training system150selects from the seed data210a tuple that has not yet been considered so as to perform these activities.

At block515, from the selected tuple, the training system150selects the original logical form235a. As described above, each tuple may include an original logical form235aand corresponding utterance225, and the training system150may select the original logical form235afrom among those.

At block520, the training system150applies the inverse seq2seq model410to the original logical form235ato cause the inverse seq2seq model to determine a second utterance225. Ideally, the second utterance225is the same as the original utterance225corresponding to the original logical form235ain the seed data210. However, this may not be the case due potentially to errors in predictions. In the early iterations of the loop, the inverse seq2seq model410may perform poorly, for instance, outputting a random utterance225(e.g., a random arrangement of words or letters) in the first iteration. In some embodiments, the inverse seq2seq model410improves as training proceeds over numerous iterations.

At block525, the training system150applies the TTS subsystem120to the second utterance225to cause the TTS subsystem120determine audio data455. The audio data455may be embodied in a sound file, such as a .wav file, for instance. Ideally, the audio data455is a perfect translation of the second utterance225and thus of the original logical form235a. However, this may not be the case due potentially to errors in predictions. In some embodiments, the TTS subsystem120may perform poorly in early iterations, for instance, outputting random audio (e.g., a random arrangement of sounds or words) in the first iteration. Further, the second utterance225received by the TTS subsystem120may incorporate an error introduced by the inverse seq2seq model410, and in some embodiments, the TTS subsystem120bases its output on the output of the inverse seq2seq model410. Thus, the output of the TTS subsystem120is impacted not only by its own history of learning, but also by the history of the inverse seq2seq model410. In some embodiments, the inverse seq2seq model410and the TTS subsystem120improve as training proceeds over numerous iterations.

At block530, the training system150applies the ASR108to the audio data455to cause the ASR108to determine a third utterance225. Ideally, the third utterance225is the same as the original utterance225corresponding to the original logical form235ain the seed data210. However, this may not be the case due potentially to errors in predictions. In the early iterations of this method500, the ASR108may perform poorly, for instance, outputting a random utterance225(e.g., a random arrangement of words or letters) in the first iteration. Further, the audio data455received by the ASR108may incorporate an error introduced by the inverse seq2seq model410or the TTS subsystem120, and in some embodiments, the ASR108bases its output on the output of the TTS subsystem120. Thus, the output of the ASR108is impacted not only by its own history of learning, but also by the histories of the inverse seq2seq model410and the TTS subsystem120. In some embodiments, the inverse seq2seq model410, the TTS subsystem120, and the ASR108improve as training proceeds over numerous iterations.

At block535, the training system150applies the semantic parser114to the third utterance225to cause the semantic parser114determine a converted logical form235b. Ideally, the converted logical form235bis the same as the original logical form235aselected from the seed data210. However, this may not be the case due potentially to errors in predictions. In the early iterations of this method500, the ASR108may perform poorly, for instance, outputting a random logical form235(e.g., a random arrangement of words and symbols) in the first iteration. Further, the third utterance225received by the semantic parser114may incorporate an error introduced by the inverse seq2seq model410, the TTS subsystem120, or the ASR108, and in some embodiments, the semantic parser114bases its output on the output of the ASR108. Thus, the output of the semantic parser114is impacted not only by its own history of learning, but also by the histories of the inverse seq2seq model410, the TTS subsystem120, and the ASR108. In some embodiments, the inverse seq2seq model410, the TTS subsystem120, the ASR108, and the semantic parser114improve as training proceeds over numerous iterations.

At block540, an objective function250is applied to the converted logical form235band the original logical form235athat was selected from the selected tuple in the seed data210. The objective function250may compare its inputs to calculate a degree of difference between such inputs, which are, in this case, the converted logical form235band the original logical form235a. Various techniques exist for constructing an appropriate objective function250for the comparison of logical forms235, and one or more of such techniques may be used to develop the objective function250used by the training system150.

At block545, the training system150trains the inverse seq2seq model410, the TTS subsystem120, the ASR108, and the semantic parser114, or a subset of these models, using backpropagation based on the result of the objective function250. In some embodiments, for instance, the TTS subsystem120is implemented as a neural network, and the training system150updates the weights of the nodes of the TTS subsystem120based on the training signal. Additionally or alternatively, the ASR108is implemented as a neural network, and the training system150updates the weights of the nodes of the ASR108based on the training signal. Additionally or alternatively, the semantic parser114is implemented as a neural network, and the training system150updates the weights of the nodes of the semantic parser114based on the training signal.

At decision block550, the training system150determines whether any tuples remain for consideration in the seed data210. If one or more tuples have not yet been considered, then the method500returns to block510to select another tuple. However, if all such tuples have been considered, then the method500ends at block555, with the ML models of the dialog system100having been trained and being useable in the dialog system100.

In some embodiments, some of the operations of the method500described above can be skipped to provide backpropagation to a proper subset of the ML models selected from the dialog system100for inclusion in the conversion subsystem240. For instance, from among the inverse seq2seq model410, the TTS subsystem120, the ASR108, and the semantic parser114in the conversion subsystem240, the training system150may train only the inverse seq2seq model410and the semantic parser114or only the TTS subsystem120and the ASR108.

Further, the conversion subsystem240is not limited to the example of including the inverse seq2seq model410, the TTS subsystem120, the ASR108, and the semantic parser114. For another example, the conversion subsystem240may include only the TTS subsystem120and the ASR108, in which case the training system150may utilize the conversion subsystem240to convert the original utterance225to audio data455and then to a converted utterance225, such that a comparison between original utterances225and corresponding converted utterances is used to train one or both of the TTS subsystem120and the ASR. Various implementations are within the scope of this disclosure.

FIG. 6is a diagram of a distributed system600for implementing certain embodiments. In the illustrated embodiment, distributed system600includes one or more client computing devices602,604,606, and608, coupled to a server612via one or more communication networks610. Clients computing devices602,604,606, and608may be configured to execute one or more applications.

In various embodiments, server612may be adapted to run one or more services or software applications that enable the use of backpropagation to train ML models of the dialog system100as described herein. For instance, server612may execute some or all aspects of the training system150or some or all aspects of the dialog system100.

In certain embodiments, server612may also provide other services or software applications that can include non-virtual and virtual environments. In some embodiments, these services may be offered as web-based or cloud services, such as under a Software as a Service (SaaS) model to the users of client computing devices602,604,606, and/or608. Users operating client computing devices602,604,606, and/or608may in turn utilize one or more client applications to interact with server612to utilize the services provided by these components. More specifically, for instance, each of client computing devices602,604,606, and/or608may be an embedded device configured to execute the dialog system100and, further, configured to communicate with server612to enable server612to train ML models of the dialog system100through backpropagation as described herein.

In the configuration depicted inFIG. 6, server612may include one or more components618,620and622that implement the functions performed by server612. These components may include software components that may be executed by one or more processors, hardware components, or combinations thereof. It should be appreciated that various different system configurations are possible, which may be different from distributed system600. The embodiment shown inFIG. 6is thus one example of a distributed system for implementing an embodiment system and is not intended to be limiting.

Users may use client computing devices602,604,606, and/or608to interact with aspects of the dialog system100provided by server612in accordance with the teachings of this disclosure. A client device may provide an interface (e.g., a speech interface) that enables a user of the client device to interact with the client device. The client device may also output information to the user via this interface. AlthoughFIG. 6depicts only four client computing devices, any number of client computing devices may be supported.

The client devices may include various types of computing systems such as PA devices, portable handheld devices, general purpose computers such as personal computers and laptops, workstation computers, wearable devices, gaming systems, thin clients, various messaging devices, sensors or other sensing devices, and the like. These computing devices may run various types and versions of software applications and operating systems (e.g., Microsoft Windows®, Apple Macintosh®, UNIX® or UNIX-like operating systems, Linux or Linux-like operating systems such as Google Chrome™ OS) including various mobile operating systems (e.g., Microsoft Windows Mobile®, iOS®, Windows Phone®, Android™, BlackBerry®, Palm OS®). Portable handheld devices may include cellular phones, smartphones, (e.g., an iPhone®), tablets (e.g., iPad®), personal digital assistants (PDAs), and the like. Wearable devices may include Google Glass® head mounted display, and other devices. Gaming systems may include various handheld gaming devices, Internet-enabled gaming devices (e.g., a Microsoft Xbox® gaming console with or without a Kinect® gesture input device, Sony PlayStation® system, various gaming systems provided by Nintendo®, and others), and the like. The client devices may be capable of executing various different applications such as various Internet-related apps, communication applications (e.g., E-mail applications, short message service (SMS) applications) and may use various communication protocols.

Server612may be composed of one or more general purpose computers, specialized server computers (including, by way of example, PC (personal computer) servers, UNIX® servers, mid-range servers, mainframe computers, rack-mounted servers, etc.), server farms, server clusters, or any other appropriate arrangement and/or combination. Server612can include one or more virtual machines running virtual operating systems, or other computing architectures involving virtualization such as one or more flexible pools of logical storage devices that can be virtualized to maintain virtual storage devices for the server. In various embodiments, server612may be adapted to run one or more services or software applications that provide the functionality described in the foregoing disclosure.

The computing systems in server612may run one or more operating systems including any of those discussed above, as well as any commercially available server operating system. Server612may also run any of a variety of additional server applications and/or mid-tier applications, including HTTP (hypertext transport protocol) servers, FTP (file transfer protocol) servers, CGI (common gateway interface) servers, JAVA® servers, database servers, and the like. Exemplary database servers include without limitation those commercially available from Oracle®, Microsoft®, Sybase®, IBM® (International Business Machines), and the like.

In some implementations, server612may include one or more applications to analyze and consolidate data feeds and/or event updates received from users of client computing devices602,604,606, and608. As an example, data feeds and/or event updates may include, but are not limited to, Twitter® feeds, Facebook® updates or real-time updates received from one or more third party information sources and continuous data streams, which may include real-time events related to sensor data applications, financial tickers, network performance measuring tools (e.g., network monitoring and traffic management applications), clickstream analysis tools, automobile traffic monitoring, and the like. Server612may also include one or more applications to display the data feeds and/or real-time events via one or more display devices of client computing devices602,604,606, and608.

Distributed system600may also include one or more data repositories614,616. These data repositories may be used to store data and other information in certain embodiments. For example, one or more of data repositories614,616may be used to store seed data210or other data required to train ML models of the dialog system100by backpropagation as described herein. Data repositories614,616may reside in a variety of locations. For example, a data repository used by server612may be local to server612or may be remote from server612and in communication with server612via a network-based or dedicated connection. Data repositories614,616may be of different types. In certain embodiments, a data repository used by server612may be a database, for example, a relational database, such as databases provided by Oracle Corporation® and other vendors. One or more of these databases may be adapted to enable storage, update, and retrieval of data to and from the database in response to SQL-formatted commands.

In certain embodiments, one or more of data repositories614,616may also be used by applications to store application data. The data repositories used by applications may be of different types such as, for example, a key-value store repository, an object store repository, or a general storage repository supported by a file system.

In certain embodiments, all or a portion of training ML models of the dialog system100by backpropagation, as described herein, may be offered as services via a cloud environment.FIG. 7is a diagram of a cloud-based system environment in which training ML models of the dialog system100by backpropagation, as described herein, may be offered at least in part as a cloud service, in accordance with certain embodiments. In the embodiment depicted inFIG. 7, cloud infrastructure system702may provide one or more cloud services that may be requested by users using one or more client computing devices704,706, and708. Cloud infrastructure system702may comprise one or more computers and/or servers that may include those described above for server612. The computers in cloud infrastructure system702may be organized as general purpose computers, specialized server computers, server farms, server clusters, or any other appropriate arrangement and/or combination.

Network(s)710may facilitate communication and exchange of data between client computing devices704,706, and708and cloud infrastructure system702. Network(s)710may include one or more networks. The networks may be of the same or different types. Network(s)710may support one or more communication protocols, including wired and/or wireless protocols, for facilitating the communications.

The embodiment depicted inFIG. 7is only one example of a cloud infrastructure system and is not intended to be limiting. It should be appreciated that, in some other embodiments, cloud infrastructure system702may have more or fewer components than those depicted inFIG. 7, may combine two or more components, or may have a different configuration or arrangement of components. For example, althoughFIG. 7depicts three client computing devices, any number of client computing devices may be supported in alternative embodiments.

The term cloud service is generally used to refer to a service that is made available to users on demand and via a communication network such as the Internet by systems (e.g., cloud infrastructure system702) of a service provider. Typically, in a public cloud environment, servers and systems that make up the cloud service provider's system are different from the customer's own on-premises servers and systems. The cloud service provider's systems are managed by the cloud service provider. Customers can thus avail themselves of cloud services provided by a cloud service provider without having to purchase separate licenses, support, or hardware and software resources for the services. For example, a cloud service provider's system may host an application, and a user may, via the Internet, on demand, order and use the application without the user having to buy infrastructure resources for executing the application. Cloud services are designed to provide easy, scalable access to applications, resources and services. Several providers offer cloud services. For example, several cloud services are offered by Oracle Corporation® of Redwood Shores, Calif., such as middleware services, database services, Java cloud services, and others.

In certain embodiments, cloud infrastructure system702may provide one or more cloud services using different models such as under a Software as a Service (SaaS) model, a Platform as a Service (PaaS) model, an Infrastructure as a Service (IaaS) model, and others, including hybrid service models. Cloud infrastructure system702may include a suite of applications, middleware, databases, and other resources that enable provision of the various cloud services.

A SaaS model enables an application or software to be delivered to a customer over a communication network like the Internet, as a service, without the customer having to buy the hardware or software for the underlying application. For example, a SaaS model may be used to provide customers access to on-demand applications that are hosted by cloud infrastructure system702. Examples of SaaS services provided by Oracle Corporation® include, without limitation, various services for human resources/capital management, customer relationship management (CRM), enterprise resource planning (ERP), supply chain management (SCM), enterprise performance management (EPM), analytics services, social applications, and others.

An IaaS model is generally used to provide infrastructure resources (e.g., servers, storage, hardware and networking resources) to a customer as a cloud service to provide elastic compute and storage capabilities. Various IaaS services are provided by Oracle Corporation®.

A PaaS model is generally used to provide, as a service, platform and environment resources that enable customers to develop, run, and manage applications and services without the customer having to procure, build, or maintain such resources. Examples of PaaS services provided by Oracle Corporation® include, without limitation, Oracle Java Cloud Service (JCS), Oracle Database Cloud Service (DBCS), data management cloud service, various application development solutions services, and others.

Cloud services are generally provided on an on-demand self-service basis, subscription-based, elastically scalable, reliable, highly available, and secure manner. For example, a customer, via a subscription order, may order one or more services provided by cloud infrastructure system702. Cloud infrastructure system702then performs processing to provide the services requested in the customer's subscription order. For example, a customer may subscribe to information services or other services provided by the dialog system100in conversational form. Cloud infrastructure system702may be configured to provide one or even multiple cloud services.

Cloud infrastructure system702may provide the cloud services via different deployment models. In a public cloud model, cloud infrastructure system702may be owned by a third party cloud services provider and the cloud services are offered to any general public customer, where the customer can be an individual or an enterprise. In certain other embodiments, under a private cloud model, cloud infrastructure system702may be operated within an organization (e.g., within an enterprise organization) and services provided to customers that are within the organization. For example, the customers may be various departments of an enterprise such as the Human Resources department, the Payroll department, etc. or even individuals within the enterprise. In certain other embodiments, under a community cloud model, the cloud infrastructure system702and the services provided may be shared by several organizations in a related community. Various other models such as hybrids of the above mentioned models may also be used.

Client computing devices704,706, and708may be of different types (such as client computing devices602,604,606, and608depicted inFIG. 6) and may be capable of operating one or more client applications. A user may use a client computing device to interact with cloud infrastructure system702, such as to request a service provided by cloud infrastructure system702. An attacker may use a client device to send malicious requests.

In some embodiments, the processing performed by cloud infrastructure system702may involve big data analysis. This analysis may involve using, analyzing, and manipulating large data sets to detect and visualize various trends, behaviors, relationships, etc. within the data. This analysis may be performed by one or more processors, possibly processing the data in parallel, performing simulations using the data, and the like. For example, big data analysis may be performed by cloud infrastructure system702for providing training of ML models by backpropagation as described herein. The data used for this analysis may include structured data (e.g., data stored in a database or structured according to a structured model) and/or unstructured data (e.g., data blobs (binary large objects)).

As depicted in the embodiment inFIG. 7, cloud infrastructure system702may include infrastructure resources730that are utilized for facilitating the provision of various cloud services offered by cloud infrastructure system702. Infrastructure resources730may include, for example, processing resources, storage or memory resources, networking resources, and the like.

In certain embodiments, to facilitate efficient provisioning of these resources for supporting the various cloud services provided by cloud infrastructure system702for different customers, the infrastructure resources730may be bundled into sets of resources or resource modules (also referred to as “pods”). Each resource module or pod may comprise a pre-integrated and optimized combination of resources of one or more types. In certain embodiments, different pods may be pre-provisioned for different types of cloud services. For example, a first set of pods may be provisioned for a database service, a second set of pods, which may include a different combination of resources than a pod in the first set of pods, may be provisioned for Java service, and the like. For some services, the resources allocated for provisioning the services may be shared between the services.

Cloud infrastructure system702may itself internally use services732that are shared by different components of cloud infrastructure system702and that facilitate the provisioning of services by cloud infrastructure system702. These internal shared services may include, without limitation, a security and identity service, an integration service, an enterprise repository service, an enterprise manager service, a virus scanning and white list service, a high availability, backup and recovery service, service for enabling cloud support, an email service, a notification service, a file transfer service, and the like.

Cloud infrastructure system702may comprise multiple subsystems. These subsystems may be implemented in software, or hardware, or combinations thereof. As depicted inFIG. 7, the subsystems may include a user interface subsystem712that enables users or customers of cloud infrastructure system702to interact with cloud infrastructure system702. User interface subsystem712may include various different interfaces such as a web interface714, an online store interface716where cloud services provided by cloud infrastructure system702are advertised and are purchasable by a consumer, and other interfaces718. For example, a customer may, using a client device, request (service request734) one or more services provided by cloud infrastructure system702using one or more of interfaces714,716, and718. For example, a customer may access the online store, browse cloud services offered by cloud infrastructure system702, and place a subscription order for one or more services offered by cloud infrastructure system702that the customer wishes to subscribe to. The service request may include information identifying the customer and one or more services that the customer desires to subscribe to.

In certain embodiments, such as the embodiment depicted inFIG. 7, cloud infrastructure system702may comprise an order management subsystem (OMS)720that is configured to process the new order. As part of this processing, OMS720may be configured to: create an account for the customer, if not done already; receive billing and/or accounting information from the customer that is to be used for billing the customer for providing the requested service to the customer; verify the customer information; upon verification, book the order for the customer; and orchestrate various workflows to prepare the order for provisioning.

Once properly validated, OMS720may then invoke an order provisioning subsystem (OPS)724that is configured to provision resources for the order including processing, memory, and networking resources. The provisioning may include allocating resources for the order and configuring the resources to facilitate the service requested by the customer order. The manner in which resources are provisioned for an order and the type of the provisioned resources may depend upon the type of cloud service that has been ordered by the customer. For example, according to one workflow, OPS724may be configured to determine the particular cloud service being requested and identify a number of pods that may have been pre-configured for that particular cloud service. The number of pods that are allocated for an order may depend upon the size/amount/level/scope of the requested service. For example, the number of pods to be allocated may be determined based upon the number of users to be supported by the service, the duration of time for which the service is being requested, and the like. The allocated pods may then be customized for the particular requesting customer for providing the requested service.

Cloud infrastructure system702may send a response or notification744to the requesting customer to indicate when the requested service is now ready for use. In some instances, information (e.g., a link) may be sent to the customer that enables the customer to start using and availing the benefits of the requested services.

Cloud infrastructure system702may provide services to multiple customers. For each customer, cloud infrastructure system702is responsible for managing information related to one or more subscription orders received from the customer, maintaining customer data related to the orders, and providing the requested services to the customer. Cloud infrastructure system702may also collect usage statistics regarding a customer's use of subscribed services. For example, statistics may be collected for the amount of storage used, the amount of data transferred, the number of users, and the amount of system up time and system down time, and the like. This usage information may be used to bill the customer. Billing may be done, for example, on a monthly cycle.

Cloud infrastructure system702may provide services to multiple customers in parallel. Cloud infrastructure system702may store information for these customers, including possibly proprietary information. In certain embodiments, cloud infrastructure system702comprises an identity management subsystem (IMS)728that is configured to manage customers information and provide the separation of the managed information such that information related to one customer is not accessible by another customer. IMS728may be configured to provide various security-related services such as identity services, such as information access management, authentication and authorization services, services for managing customer identities and roles and related capabilities, and the like.

FIG. 8is a diagram of an example computer system800that may be used to implement certain embodiments. For example, in some embodiments, computer system800may be used to implement any of systems, subsystems, and components described herein. For example, multiple host machines may provide and implement training of ML models of the dialog system100by backpropagation as described herein. Computer systems such as computer system800may be used as host machines. As shown inFIG. 8, computer system800includes various subsystems including a processing subsystem804that communicates with a number of other subsystems via a bus subsystem802. These other subsystems may include a processing acceleration unit806, an I/O subsystem808, a storage subsystem818, and a communications subsystem824. Storage subsystem818may include non-transitory computer-readable storage media including storage media822and a system memory810.

Processing subsystem804controls the operation of computer system800and may comprise one or more processors, application specific integrated circuits (ASICs), or field programmable gate arrays (FPGAs). The processors may include be single core or multicore processors. The processing resources of computer system800can be organized into one or more processing units832,834, etc. A processing unit may include one or more processors, one or more cores from the same or different processors, a combination of cores and processors, or other combinations of cores and processors. In some embodiments, processing subsystem804can include one or more special purpose co-processors such as graphics processors, digital signal processors (DSPs), or the like. In some embodiments, some or all of the processing units of processing subsystem804can be implemented using customized circuits, such as application specific integrated circuits (ASICs), or field programmable gate arrays (FPGAs).

In some embodiments, the processing units in processing subsystem804can execute instructions stored in system memory810or on computer-readable storage media822. In various embodiments, the processing units can execute a variety of programs or code instructions and can maintain multiple concurrently executing programs or processes. At any given time, some or all of the program code to be executed can be resident in system memory810and/or on computer-readable storage media822including potentially on one or more storage devices. Through suitable programming, processing subsystem804can provide various functionalities described above. In instances where computer system800is executing one or more virtual machines, one or more processing units may be allocated to each virtual machine.

In certain embodiments, a processing acceleration unit806may optionally be provided for performing customized processing or for off-loading some of the processing performed by processing subsystem804so as to accelerate the overall processing performed by computer system800.

Storage subsystem818provides a repository or data store for storing information and data that is used by computer system800. Storage subsystem818provides a tangible non-transitory computer-readable storage medium for storing the basic programming and data constructs that provide the functionality of some embodiments. Storage subsystem818may store software (e.g., programs, code modules, instructions) that when executed by processing subsystem804provides the functionality described above. The software may be executed by one or more processing units of processing subsystem804. Storage subsystem818may also provide a repository for storing data used in accordance with the teachings of this disclosure.

Storage subsystem818may include one or more non-transitory memory devices, including volatile and non-volatile memory devices. As shown inFIG. 8, storage subsystem818includes a system memory810and a computer-readable storage media822. System memory810may include a number of memories including a volatile main random access memory (RAM) for storage of instructions and data during program execution and a non-volatile read only memory (ROM) or flash memory in which fixed instructions are stored. In some implementations, a basic input/output system (BIOS), containing the basic routines that help to transfer information between elements within computer system800, such as during start-up, may typically be stored in the ROM. The RAM typically contains data and/or program modules that are presently being operated and executed by processing subsystem804. In some implementations, system memory810may include multiple different types of memory, such as static random access memory (SRAM), dynamic random access memory (DRAM), and the like.

By way of example, and not limitation, as depicted inFIG. 8, system memory810may load application programs812that are being executed, which may include various applications such as Web browsers, mid-tier applications, relational database management systems (RDBMS), etc., program data814, and an operating system816. By way of example, operating system816may include various versions of Microsoft Windows®, Apple Macintosh®, and/or Linux operating systems, a variety of commercially-available UNIX® or UNIX-like operating systems (including without limitation the variety of GNU/Linux operating systems, the Google Chrome® OS, and the like) and/or mobile operating systems such as iOS, Windows® Phone, Android® OS, BlackBerry® OS, Palm® OS operating systems, and others.

In certain embodiments, software instructions or code implementing training of ML models of the dialog system100by backpropagation, as described herein, may be executed in system memory810.

In certain embodiments, storage subsystem818may also include a computer-readable storage media reader820that can further be connected to computer-readable storage media822. Reader820may receive and be configured to read data from a memory device such as a disk, a flash drive, etc.

In certain embodiments, computer system800may support virtualization technologies, including but not limited to virtualization of processing and memory resources. For example, computer system800may provide support for executing one or more virtual machines. In certain embodiments, computer system800may execute a program such as a hypervisor that facilitated the configuring and managing of the virtual machines. Each virtual machine may be allocated memory, compute (e.g., processors, cores), I/O, and networking resources. Each virtual machine generally runs independently of the other virtual machines. A virtual machine typically runs its own operating system, which may be the same as or different from the operating systems executed by other virtual machines executed by computer system800. Accordingly, multiple operating systems may potentially be run concurrently by computer system800.

Communications subsystem824provides an interface to other computer systems and networks. Communications subsystem824serves as an interface for receiving data from and transmitting data to other systems from computer system800. For example, communications subsystem824may enable computer system800to establish a communication channel to one or more client devices via the Internet for receiving and sending information from and to the client devices.

Communication subsystem824may support both wired and/or wireless communication protocols. For example, in certain embodiments, communications subsystem824may include radio frequency (RF) transceiver components for accessing wireless voice and/or data networks (e.g., using cellular telephone technology, advanced data network technology, such as 3G, 4G or EDGE (enhanced data rates for global evolution), WiFi (IEEE 802.XX family standards, or other mobile communication technologies, or any combination thereof), global positioning system (GPS) receiver components, and/or other components. In some embodiments communications subsystem824can provide wired network connectivity (e.g., Ethernet) in addition to or instead of a wireless interface.

Communication subsystem824can receive and transmit data in various forms. For example, in some embodiments, in addition to other forms, communications subsystem824may receive input communications in the form of structured and/or unstructured data feeds826, event streams828, event updates830, and the like. For example, communications subsystem824may be configured to receive (or send) data feeds826in real-time from users of social media networks and/or other communication services such as Twitter® feeds, Facebook® updates, web feeds such as Rich Site Summary (RSS) feeds, and/or real-time updates from one or more third party information sources.

Communications subsystem824may also be configured to communicate data from computer system800to other computer systems or networks. The data may be communicated in various different forms such as structured and/or unstructured data feeds826, event streams828, event updates830, and the like to one or more databases that may be in communication with one or more streaming data source computers coupled to computer system800.

Computer system800can be one of various types, including a handheld portable device (e.g., an iPhone® cellular phone, an iPad® computing tablet, a PDA), a wearable device (e.g., a Google Glass® head mounted display), a personal computer, a workstation, a mainframe, a kiosk, a server rack, or any other data processing system. Due to the ever-changing nature of computers and networks, the description of computer system800depicted inFIG. 8is intended only as a specific example. Many other configurations having more or fewer components than the system depicted inFIG. 8are possible. Based on the disclosure and teachings provided herein, a person of ordinary skill in the art will appreciate other ways and/or methods to implement the various embodiments.

Although specific embodiments have been described, various modifications, alterations, alternative constructions, and equivalents are possible. Embodiments are not restricted to operation within certain specific data processing environments, but are free to operate within a plurality of data processing environments. Additionally, although certain embodiments have been described using a particular series of transactions and steps, it should be apparent to those skilled in the art that this is not intended to be limiting. Although some flowcharts describe operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be rearranged. A process may have additional steps not included in the figure. Various features and aspects of the above-described embodiments may be used individually or jointly.

Further, while certain embodiments have been described using a particular combination of hardware and software, it should be recognized that other combinations of hardware and software are also possible. Certain embodiments may be implemented only in hardware, or only in software, or using combinations thereof. The various processes described herein can be implemented on the same processor or different processors in any combination.

Specific details are given in this disclosure to provide a thorough understanding of the embodiments. However, embodiments may be practiced without these specific details. For example, well-known circuits, processes, algorithms, structures, and techniques have been shown without unnecessary detail in order to avoid obscuring the embodiments. This description provides example embodiments only, and is not intended to limit the scope, applicability, or configuration of other embodiments. Rather, the preceding description of the embodiments will provide those skilled in the art with an enabling description for implementing various embodiments. Various changes may be made in the function and arrangement of elements.