Patent Description:
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.

<NPL> discloses a data-driven framework to augment training data, wherein one utterance's alternative expressions of the same semantic are leveraged to train seq2seq model. <NPL> discloses an adversarial training method for the multi-task and multi-lingual joint modeling needed for utterance intent classification.

<NPL> discloses a unified neural network that jointly performs domain, intent, and slot predictions.

Embodiments not falling within the scope of the claims are exemplary and considered useful for understanding the invention.

A dialog system is a voice-enabled system capable of having a dialog with a user, such as via speech inputs and audio outputs. Typically, a dialog system includes ones or multiple machine learning (ML) models, such as a semantic parser. The present disclosure relates to techniques for using a generative adversarial network (GAN) to train a semantic parser of a dialog system. For instance, in some embodiments, a GAN includes a semantic parser as its generator and further includes a discriminator and an error-minimization module. In some embodiments, the semantic parser and the discriminator are ML models trained as adversaries, causing both ML models to improve jointly.

In some embodiments, seed data is used as training input into the GAN. The seed data may include a set of seed tuples, each seed tuple including an utterance and a logical form corresponding to the utterance. An embodiment described herein trains the semantic parser and the discriminator jointly, by first fixing the semantic parser while training the discriminator based on semantic parser output and then fixing the discriminator while training the semantic parser based on discriminator output, and repeating until the seed data is used up.

While the semantic parser is fixed and thus static, the semantic parser may generate logical forms based on randomly selected utterances. Together, each pair of a randomly selected utterance and the corresponding logical form, as generated by the semantic parser, may form a generated tuple. The discriminator may receive as input the generated tuples along with seed tuples. For each such tuple, the discriminator may output (i.e., predict) a probability that the tuple is authentic or, in other words, a probability that the tuple is a seed tuple. The error-minimization module may generate a training signal to train the discriminator based on a divergence of the discriminator's predictions as compared to an accurate distribution (i.e., an accurate sequence of values) indicating whether the tuples are authentic.

When the discriminator is fixed and thus static, the semantic parser may generate logical forms based on utterances selected from seed tuples of the seed data. Together, each pair of an utterance from the seed data and a corresponding logical form, as generated by the semantic parser, may form a tuple. The discriminator may receive such tuples as input and, for each tuple, may predict a probability that the tuple is authentic. The error-minimization module may generate a training signal to train the semantic parser based on a divergence of the discriminator's actual predictions of authenticity as compared to a desired set of predictions in which all generated logical forms are predicted to be authentic. In other words, the semantic parser is trained with an aim to fool the discriminator into deeming all generated logical forms to be authentic.

As a result, the semantic parser and the discriminator may be trained jointly as members of the GAN, with the semantic parser learning to generate logical forms and the discriminator learning to recognize logical forms. Eventually, as the discriminator improves and thus causes the semantic parser to improve, the semantic parser may become so good at generating logical forms that the logical forms generated are indistinguishable from logical forms belonging the seed data. In this manner, the semantic parser is trained to generate logical forms based on utterances.

In other embodiments, a dialog system may include a speech input module for receiving speech input, a speech output module for outputting speech output to a user, and a dialog processing module for performing or causing one or more actions to be performed based upon interpretations of the speech inputs and preparing appropriate responses to the user. The dialog processing module may utilize a semantic parser obtained by using the above mentioned techniques for using a GAN to train a semantic parser of a dialog system.

The foregoing, together with other features and embodiments will become more apparent upon referring to the following specification, claims, and accompanying drawings.

In the following description, for the purposes of explanation, specific details are set forth in order to provide a thorough understanding of certain embodiments. However, it will be apparent that various embodiments may be practiced without these specific details. The figures and description are not intended to be restrictive. " Any embodiment or design described herein as "exemplary" or as an "example" is not necessarily to be construed as preferred or advantageous over other embodiments or designs.

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, and a large amount of training data is needed to train the various machine learning models of the dialog system. One of such ML models is a semantic parser subsystem, also referred to as a semantic parser. Generally, the semantic parser receives an utterance representing speech input provided by a user, where the utterance is a textual representation of natural language. The semantic parser maps that utterance to a logical form, which is a representation of the utterance as translated into a logic-based language conforming to an established grammar and thus parseable by a dialog manager subsystem of the dialog system. The dialog manager subsystem then parses and processes the logical form to determine how to respond.

To train the semantic parser subsystem, a training system associated with the dialog system typically requires a large quantity of tuples, each tuple including an utterance (e.g., a textual representation of natural language) and a corresponding logical form. When an insufficient amount of training data is used, the semantic parser may be less effective than desired and may introduce errors into a logical form and, thus, into a workflow of the dialog system. As a result of the introduction of an error in the logical form, the dialog manager subsystem might then fail to generate an appropriate response to the user.

A generative adversarial network (GAN) is an architecture that trains a generator and a discriminator in an adversarial manner. GANs are typically used in image analysis and, particularly, in the field of machine vision. In an example conventional GAN, an image generator generates images intended to be in a specific class (e.g., images of faces), and the discriminator determines whether the generated images are indeed in that class. By comparing the outputs of the image generator and the discriminator to accurate distributions of outputs, the GAN provides a training signal to the image generator and to the discriminator to train the image generator and the discriminator. GANs are particularly useful when the training data available is smaller than would be ideal for individual training, because the adversarial nature can lead to effective training with a relatively small set of training data.

In some embodiments of a training system described herein, a semantic parser of a dialog system is trained in a GAN. Specifically, the semantic parser may behave as a generator of the GAN and, as such, may learn generate logical forms, and a discriminator may learn to distinguish between authentic and inauthentic logical forms. An error-minimization module may apply one or more objective functions to provide a training signal to train the semantic parser and the discriminator. After training, the semantic parser may be used to map utterances to logical forms as part of the dialog system. This manner of training the semantic parser may be more effective than the traditional technique of training the semantic parser on an individual basis, due to the adversarial nature of training in a GAN.

<FIG> is a diagram of an example of a dialog system <NUM>, according to certain embodiments described herein, which utilizes a semantic parser <NUM> trained in a GAN <NUM>. The dialog system <NUM> is configured to receive speech inputs <NUM>, also referred to as voice inputs, from a user <NUM>, such as through a speech input module. For example, the speech input module receives speech inputs from a microphone or another device that can obtain speech or voice. The dialog system <NUM> may then interpret the speech inputs <NUM>. The dialog system <NUM> may maintain a dialog with a user <NUM> and may possibly perform or cause one or more actions to be performed based upon interpretations of the speech inputs <NUM>, for instance, by. using a dialog processing module which utilizes the semantic parser <NUM>. The dialog system <NUM> may prepare appropriate responses, such as through using the dialog processing module, and may output the responses to the user using voice or speech output, also referred to as audio output, such as by way of a speech output module. For example, the speech output module can be a speaker or another device that can output voice or speech. The dialog system <NUM> is 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 in <FIG> are provided for illustrative purposes. Different numbers of devices may be used. For example, while each device, server, and system in <FIG> is shown as a single device, multiple devices may be used instead.

In certain embodiments, the processing performed by the dialog system <NUM> is implemented by a pipeline of components or subsystems, including a speech input component <NUM>; a wake-word detection (WD) subsystem <NUM>; an automatic speech recognition (ASR) subsystem <NUM>; a natural language understanding (NLU) subsystem <NUM>, which includes a named entity recognizer (NER) subsystem <NUM> and a semantic parser subsystem <NUM>; a dialog manager (DM) subsystem <NUM>; a natural language generator (NLG) subsystem <NUM>; a text-to-speech (TTS) subsystem <NUM>; and a speech output component <NUM>. 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 component <NUM> includes hardware and software configured to receive speech input <NUM>. In some instances, the speech input component <NUM> may be part of the dialog system <NUM>. In some other instances, the speech input component <NUM> may be separate from and be communicatively coupled to the dialog system <NUM>. The speech input component <NUM> may, for example, include a microphone coupled to software configured to digitize and transmit speech input <NUM> to the wake-word detection subsystem <NUM>.

The wake-word detection (WD) subsystem <NUM> is 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 system <NUM>, the WD subsystem <NUM> is configured to activate the ASR subsystem <NUM>. In certain implementations, a user may be provided the ability to activate or deactivate the WD subsystem <NUM> (e.g., by pushing a button) to cause the WD subsystem <NUM> to listen for or stop listening for the wake-word. When activated, or when operating in active mode, the WD subsystem <NUM> is configured to continuously receive an audio input stream and process the audio input stream to identify audio input, such as speech input <NUM>, corresponding to the wake-word. When audio input corresponding to the wake-word is detected, the WD subsystem <NUM> activates the ASR subsystem <NUM>.

As described above, the WD subsystem <NUM> activates the ASR subsystem <NUM>. In some implementations of the dialog system <NUM>, mechanisms other than wake-word detection may be used to trigger or activate the ASR subsystem <NUM>. For example, in some implementations, a push button on a device may be used to trigger the ASR subsystem <NUM> without needing a wake-word. In such implementations, the WD subsystem <NUM> need not be provided. When the push button is pressed or activated, the speech input <NUM> received after the button activation is provided to the ASR subsystem <NUM> for processing. Additionally or alternatively, in some implementations, the ASR subsystem <NUM> may be activated upon receiving an input to be processed.

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

The NLU subsystem <NUM> receives utterances generated by the ASR subsystem <NUM>. The utterances received by the NLU subsystem <NUM> from the ASR subsystem <NUM> may include text utterances corresponding to spoken words, phrases, clauses, or the like. The NLU subsystem <NUM> translates each utterance, or a series of utterances, to a corresponding logical form.

In certain implementations, the NLU subsystem <NUM> includes a named entity recognizer (NER) subsystem <NUM> and a semantic parser subsystem <NUM>. The NER subsystem <NUM> receives 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 subsystem <NUM>, 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 subsystem <NUM> can 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 subsystem <NUM> may 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 subsystem <NUM> are then fed to the DM subsystem <NUM> for further processing.

As shown in <FIG>, in some embodiments, a training system <NUM> described herein trains the semantic parser subsystem <NUM>, also referred to herein as the semantic parser <NUM>, as part of a generative adversarial network <NUM> to prepare the semantic parser <NUM> for its operation in the dialog system <NUM>. For instance, the training system <NUM> utilizes the GAN <NUM> to train the semantic parser <NUM> to perform the tasks described above for determining a logical form based on one or more utterances. In some embodiments, the dialog system <NUM> is improved over a conventional dialog system by the use of this adversarial technique of training the semantic parser <NUM>.

The DM subsystem <NUM> is configured to manage a dialog with the user based on logical forms received from the NLU subsystem <NUM>. As part of the dialog management, the DM subsystem <NUM> is 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 subsystem <NUM> is configured to interpret the intents identified in the logical forms received from the NLU subsystem <NUM>. Based on the interpretations, the DM subsystem <NUM> may initiate one or more actions that it interprets as being requested by the speech inputs <NUM> provided by the user. In certain embodiments, the DM subsystem <NUM> performs dialog-state tracking based on current and past speech inputs <NUM> and based on a set of rules (e.g., dialog policies) configured for the DM subsystem <NUM>. 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 subsystem <NUM> also generates responses to be communicated back to the user involved in the dialog. These responses may be based upon actions initiated by the DM subsystem <NUM> and their results. The responses generated by the DM subsystem <NUM> are fed to the NLG subsystem <NUM> for further processing.

The NLG subsystem <NUM> is configured to generate natural language texts corresponding to the responses generated by the DM subsystem <NUM>. The texts may be generated in a form that enables them to be converted to speech by the TTS subsystem <NUM>. The TTS subsystem <NUM> receives the texts from the NLG subsystem <NUM> and 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 component <NUM> of the dialog system (e.g., a speaker, or communication channel coupled to an external speaker). In some instances, the speech output component <NUM> may be part of the dialog system <NUM>. In some other instances, the speech output component <NUM> may be separate from and communicatively coupled to the dialog system <NUM>.

As described above, the various subsystems of the dialog system <NUM> working in cooperation provide the functionality that enables the dialog system <NUM> to receive speech inputs <NUM> and to respond using speech outputs <NUM> and, 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 system <NUM> described above may be implemented entirely on the device with which the user interacts. In some other implementations, some components or subsystems of the dialog system <NUM> may 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> is a diagram of the training system <NUM> configured to train the semantic parser <NUM> of the dialog system <NUM> through the use of a generative adversarial network <NUM>, according to certain embodiments described herein. More specifically, in some embodiments, the semantic parser <NUM> acts as a generator of the GAN <NUM>, and the training system <NUM> trains the semantic parser <NUM> jointly with a discriminator <NUM> in the GAN <NUM>. An error-minimization module <NUM> in the GAN <NUM> may provide one or more training signals back to the semantic parser <NUM> and the discriminator <NUM> to train the semantic parser <NUM> and the discriminator <NUM>, more specifically, using backpropagation to train the semantic parser <NUM>. A data-input subsystem <NUM> of the training system <NUM> may provide inputs, such as input based on seed data <NUM>, to the semantic parser <NUM>, the discriminator <NUM>, and the error-minimization module <NUM> to enable the semantic parser <NUM>, the discriminator <NUM>, and the error-minimization module <NUM> to perform the tasks described herein. After training, the semantic parser <NUM> may be used as part of a dialog system <NUM> such as that shown in <FIG>.

In some embodiments, the training system <NUM> is implemented as a computing device or portion thereof, such as a server. The training system <NUM> may 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 system <NUM> may be embodied in program code implementing the training system <NUM>, where such program code is executable by one or more processing units. For instance, the semantic parser <NUM>, the discriminator <NUM>, the error-minimization module <NUM>, and other aspects of the GAN <NUM> may each be implemented as one or more software functions or specialized hardware devices and may operate together to implement the training system <NUM> as described herein. The semantic parser <NUM> and the discriminator <NUM> may each be implemented as a respective machine learning model, such as a neural network.

As shown in <FIG>, the training system <NUM> may include a GAN <NUM>, which includes a generator and a discriminator <NUM> trained jointly. Specifically, the generator may be the semantic parser <NUM> of a dialog system <NUM>, and thus, the semantic parser <NUM> is trained within the GAN <NUM> before, or while, being integrated into the dialog system <NUM>. Specifically, the training system <NUM> may train the semantic parser <NUM> to map utterances <NUM> to logical forms <NUM>, and the training system <NUM> may train the discriminator <NUM> to determine whether a logical form <NUM> is authentic (i.e., shares a seed tuple with its corresponding utterance <NUM> in a set of seed data <NUM>). Further, an error-minimization module <NUM> updates the semantic parser <NUM> and the discriminator <NUM> to minimize errors between expected predictions and actual predictions made in the GAN <NUM>. In some embodiments, as part of the GAN <NUM>, the semantic parser <NUM> and the discriminator <NUM> are trained jointly to cause each of the semantic parser <NUM> and the discriminator <NUM> to improve as the other improves. As a result, the semantic parser <NUM> learns to map utterances <NUM>, such as utterances <NUM> tagged by the NER subsystem <NUM>, to logical forms <NUM> useable by the DM subsystem <NUM>.

In some embodiments, the semantic parser <NUM> is a neural network, such as a sequence-to-sequence (seq2seq) model, for determining logical forms <NUM> based on utterances <NUM>. Conventionally, a semantic parser is trained with training data including a set of tuples, each tuple having an utterance and a corresponding logical form. As described herein, however, in some embodiments, the semantic parser <NUM> is trained as part of the GAN <NUM>. As further described herein, the training utilizes seed data <NUM> that includes seed tuples, each seed tuple including an utterance <NUM> and a corresponding logical form <NUM>. However, the training also incorporates output from the discriminator <NUM>.

The discriminator <NUM> may be a binary classifier that maps a tuple to a class; for instance, the discriminator <NUM> may be implemented as a neural network. The discriminator <NUM> may receive as input a tuple including an utterance <NUM> and a logical form <NUM>, and the discriminator <NUM> may output an indicator of whether the logical form <NUM> is an authentic representation of the utterance <NUM>. For instance, the discriminator <NUM> may output a probability that the logical form <NUM> is authentic (i.e., paired with the utterance <NUM> in the seed data <NUM>), and thus accurate. In some embodiments, to achieve this, the discriminator <NUM> may be trained jointly with the semantic parser <NUM> in the GAN <NUM> as described herein.

The error-minimization module <NUM> may provide a training signal to train the discriminator <NUM> and to train the semantic parser <NUM> by way of backpropagation. For instance, to train the discriminator <NUM>, the error-minimization module <NUM> may utilize a first objective function <NUM> to compare the outputs from the discriminator <NUM>, which are indications of whether logical forms <NUM> received are authentic, to an accurate distribution of indications of authenticity of those logical forms <NUM>. The error-minimization module <NUM> may use the result of the first objective function <NUM> to train the discriminator <NUM> to make better predictions of authenticity.

To train the semantic parser <NUM>, the error-minimization module <NUM> may utilize a second objective function <NUM>, which may be the same as the first objective function <NUM>, to compare the outputs from the discriminator <NUM>, indicating predictions of authenticity of logical forms <NUM> output of the semantic parser <NUM>, to a selected distribution (i.e., a desired distribution) of indications of authenticity of those logical forms <NUM>. That selected distribution may be a distribution indicating that all logical forms <NUM> output by the semantic parser <NUM> are authentic. This is because, in some embodiments, the training system <NUM> seeks to provide a semantic parser <NUM> whose outputs are always deemed authentic. One of skill in the art will understand how to construct such objective functions <NUM>. In some embodiments, although the semantic parser <NUM> and the discriminator <NUM> are trained jointly within the GAN <NUM>, no more than one of the semantic parser <NUM> and the generator is actively being updated based on the training signal from the error-minimization module <NUM> at a given time.

<FIG> is a diagram of a method <NUM> of training the semantic parser <NUM> jointly with a discriminator <NUM> in a GAN <NUM>, according to certain embodiments described herein. In some embodiments, this method <NUM> or similar is performed prior to the semantic parser <NUM> being used in a dialog system <NUM>. Through the training described herein, the semantic parser <NUM> may learn to translate (i.e., to map) utterances <NUM> to logical forms <NUM> representing such utterances <NUM>, which is a task performed by the semantic parser <NUM> as part of the dialog system <NUM>.

The method <NUM> depicted in <FIG>, 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 method <NUM> is intended to be illustrative and non-limiting. Although <FIG> depicts 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 method <NUM> may be performed in parallel. In certain embodiments, the method <NUM> may be performed by the training system <NUM>.

As shown in <FIG>, at block <NUM>, the data-input subsystem <NUM> of the training system <NUM> obtains seed data <NUM>. The seed data <NUM> may include a set of seed tuples, with each seed tuple including an utterance <NUM> and a corresponding logical form <NUM>. In each seed tuple, the logical form <NUM> is an accurate and authentic representation, or translation, of the utterance <NUM>. It will be understood that various techniques exist for obtaining the seed data <NUM>. For instance, the seed data <NUM> may be manually determined, such as through crowdsourcing, and then stored in a memory device accessible by the data-input subsystem <NUM>.

At block <NUM>, the training system <NUM> begins a new round of training. Block <NUM> is the beginning of an iterative loop. In some embodiments, during each iteration of the loop, the training system <NUM> conducts a round of training. Each round of training may include training the discriminator <NUM> during part of the round and training the semantic parser <NUM> during another part of the round. Although these two parts of the round may be performed in parallel, the two parts are performed sequentially in some embodiments to enable training of the semantic parser <NUM> in the second part to benefit from training of the discriminator <NUM> in the first part. Further, the training system <NUM> may utilize a different subset of the seed data <NUM> for each round of training the discriminator <NUM> and the semantic parser <NUM> and, as such, for each iteration of the loop.

At block <NUM>, the training system <NUM> conducts training of the discriminator <NUM> based on a first portion of the seed data <NUM> obtained at block <NUM>. In some embodiments, this first portion of the seed data <NUM> has not yet been used for training in a prior round. During this training of the discriminator <NUM>, the semantic parser <NUM> may be fixed to enable updating of the discriminator <NUM> based on the discriminator's predictions as to the authenticity of output from the semantic parser <NUM>, without variation in how the semantic parser <NUM> determines that output. Activities involved in training the discriminator <NUM> during a round of training are described in more detail below, with reference to <FIG>.

At block <NUM>, the training system <NUM> conducts training of the semantic parser <NUM> based on a second portion of the seed data <NUM> obtained at block <NUM>. In some embodiments, this second portion of the seed data <NUM> is a different from the first portion used at block <NUM> (e.g., has no overlap in seed tuples) and has not yet been used for training in a prior round. During this training of the semantic parser <NUM>, the discriminator <NUM> may be fixed to enable updating of the semantic parser <NUM> based on the discriminator's predictions as to the authenticity of output from the semantic parser <NUM>, without variation in how the discriminator <NUM> determines that authenticity. Activities involved in training the semantic parser <NUM> during a round of training are described in more detail below, with reference to <FIG>.

At decision block <NUM>, the training system <NUM> determines whether all the seed data <NUM> obtained at block <NUM> has been selected and used for some round of training (i.e., some iteration of the loop) as described above. If not all the seed data <NUM> has been used, the method <NUM> may return to block <NUM>, where another round of training begins. If all the seed data <NUM> has been used in the training, however, then the method <NUM> may end at block <NUM>, at which time the semantic parser <NUM> has been trained and may be ready for use in the dialog system <NUM>.

<FIG> is a diagram of a method <NUM> of training the discriminator <NUM> as part of a round of training in the GAN <NUM>, according to certain embodiments described herein. Specifically, the training system <NUM> may perform this method <NUM> or similar at block <NUM> of the above method <NUM>, to train the discriminator <NUM> within a single round of training. Thus, this method <NUM> or similar may be performed once per iteration according to some embodiments.

At block <NUM>, the data-input subsystem <NUM> of the training system <NUM> selects a first portion of the seed data <NUM>, where the first portion has not been used in prior rounds of training. In some embodiments, this first portion of the seed data <NUM> is a subset of the seed tuples in the seed data <NUM>; for instance, the first portion of the seed data <NUM> may be a proper subset of the seed tuples in the seed data <NUM> such that not all seed tuples of the seed data <NUM> are included in the first portion.

At block <NUM>, the semantic parser <NUM> is fixed while the discriminator <NUM> is open for updates. In other words, in some embodiments, the semantic parser <NUM> will remain static for the time being to enable the discriminator <NUM> to be trained based on the current state of the semantic parser <NUM>.

In some embodiments, block <NUM> and block <NUM> are performed in parallel. At block <NUM>, the data-input subsystem <NUM> of the training system <NUM> inputs a stream of utterances <NUM> to the semantic parser <NUM>, which generates logical forms <NUM> based on the stream of utterances <NUM> input to the semantic parser <NUM>. The utterances <NUM> in the stream of utterances <NUM> may be utterances <NUM> that are not from the seed data <NUM> but are, for instance, generated randomly or selected randomly by the data-input subsystem <NUM> or by some other component. For example, and not by way of limitation, the utterances <NUM> may be selected from a corpus (e.g., one or more books or articles) written in natural language. For each such utterance <NUM> received, the semantic parser <NUM> may generate a logical form <NUM>. Early in the training, the semantic parser's output of logical forms <NUM> may be poor. For instance, the logical forms <NUM> generated may be random (e.g., a random arrangement of words and symbols). However, as training proceeds throughout the round or over multiple rounds, the semantic parser <NUM> may improve.

At block <NUM>, which may be performed in parallel with block <NUM>, the data-input subsystem <NUM> of the training system <NUM> may input to the discriminator <NUM> a stream of tuples, and the discriminator <NUM> may generate predictions of authenticity for those tuples. Each tuple in the stream of tuples may include an utterance <NUM> and a logical form <NUM>. The data-input subsystem <NUM> may provide this stream of tuples as a combination (e.g., a random or arbitrary combination) of (<NUM>) seed tuples from the first portion of the seed data <NUM>, selected at block <NUM>, and (<NUM>) tuples having logical forms <NUM> that have been generated by the semantic parser <NUM> at block <NUM>. Based on the stream of tuples, the discriminator <NUM> may output predictions of authenticity corresponding to the tuples. In other words, given a tuple, the discriminator <NUM> may predict whether the logical form <NUM> in the tuple is an authentic representation of the utterance <NUM> in the tuple, or in other words, the discriminator <NUM> may guess whether the tuple is part of the seed data <NUM> rather than the logical form <NUM> having been generated by the semantic parser <NUM>. The tuples received by the discriminator <NUM> may include both seed tuples, which are in the first portion of the seed data <NUM>, and generated tuples, each of which includes an utterances <NUM> received by the semantic parser <NUM> and the corresponding logical form <NUM> generated by the semantic parser <NUM> at block <NUM>.

In some embodiments, the discriminator <NUM> is not notified of which tuples are from the seed data <NUM>, and are thus authentic, and which tuples are from the semantic parser <NUM>, and are thus generated. For each tuple, the discriminator <NUM> may classify the tuple based on deemed authenticity. To this end, the discriminator <NUM> may output a score indicating its determination (i.e., prediction) of the likelihood that the logical form <NUM> is an authentic representation of the corresponding utterance <NUM> in the tuple; for instance, the score is a probability that the logical form <NUM> is an authentic representation of the corresponding utterance <NUM> in the tuple and, thus, that the tuple is from the seed data <NUM>. Ideally, the discriminator <NUM> outputs a high probability, such as <NUM>, for an authentic tuple (i.e., a seed tuple) and a low probability, such as <NUM>, for a generated tuple. Early in the training, the discriminator's output may be poor. For instance, the probabilities output may be random numbers in a fixed range (e.g., between <NUM> and <NUM> inclusively). However, as training proceeds throughout the round or over multiple rounds, the discriminator <NUM> may improve.

At block <NUM>, the error-minimization module <NUM> of the training system <NUM> generates a training signal to train the discriminator <NUM> based on the outputs of the semantic parser <NUM> and the discriminator <NUM>, as determined at block <NUM> and block <NUM>. In some embodiments, the data-input subsystem <NUM> of the training system <NUM> provides the first portion of the seed data <NUM>, as accessed at block <NUM>, to the error-minimization module <NUM> to enable the error-minimization module <NUM> to determine the training signal.

For instance, the error-minimization module <NUM> may apply a first objective function <NUM> that compares an accurate distribution (i.e., an accurate sequence of values) of predictions to the actual distribution (i.e., the actual sequence of values) of predictions from the discriminator <NUM> so as to determine a divergence, or degree of difference, between those two distributions. The error-minimization module <NUM> may have access to the first portion of the seed data <NUM> and may thus be aware of which tuples received by the discriminator <NUM> are authentic and which tuples are generated. As such, the error-minimization module <NUM> is aware that an accurate distribution assigns a value of <NUM> (i.e., one hundred percent probability) to seed tuples and a value of <NUM> (i.e., zero percent probability) to generated tuples, which are not authentic. Thus, the error-minimization module <NUM> may use the first objective function <NUM> to compare this accurate distribution to the actual output of the discriminator <NUM>, so as to provide a training signal to the discriminator <NUM>. The training signal represents an error in the predictions made by the discriminator <NUM>. Based on the training signal, the training system <NUM> may update the discriminator <NUM> to reduce the error between the accurate distribution and the actual distribution. For instance, the nodes of the neural network acting as the discriminator <NUM> may be updated based on the training signal.

In some embodiments, as shown in <FIG>, block <NUM> is performed following block <NUM> and block <NUM>. In that case, the error-minimization module <NUM> may provide the training signal only after the discriminator <NUM> has received all tuples being used to train the discriminator <NUM> in this round of training. As such, the discriminator <NUM> may be updated once based on a batch that includes the entire first portion of the seed data <NUM> and the generated tuples described above. Alternatively, however, block <NUM> may be performed in parallel with block <NUM> and block <NUM>, such that a training signal is sent back to the discriminator <NUM> while the discriminator <NUM> is evaluating inputs, such that the discriminator <NUM> is updated while the first portion of the seed data <NUM> and the generated tuples are still being provided.

At block <NUM>, the training system <NUM> ends training of the discriminator <NUM> in the current round of training. As such, the semantic parser <NUM>, which was fixed at block <NUM>, is unlocked and need not remain fixed as training proceeds.

<FIG> is a diagram of a method of training the semantic parser as part of a round of training in the GAN <NUM>, according to certain embodiments described herein. Specifically, the training system <NUM> may perform this method <NUM> or similar at block <NUM> of the above method <NUM>, to train the semantic parser <NUM> within a single round of training. Thus, this method <NUM> or similar may be performed once per iteration according to some embodiments.

At block <NUM>, the data-input subsystem <NUM> of the training system <NUM> selects a second portion of the seed data <NUM>, where the second portion has not been used in prior rounds of training. In some embodiments, this second portion of the seed data <NUM> is a subset of the seed tuples in the seed data <NUM>; for instance, the second portion of the seed data <NUM> may be a proper subset of the seed tuples in the seed data <NUM> such that not all seed tuples of the seed data <NUM> are included in the second portion. Additionally, in some embodiments, the second portion of the seed data <NUM> is different from the first portion; alternatively, however, the second portion may be same as the first portion of the seed data <NUM>. In either case, the remainder of the seed data <NUM> outside of the first portion and the second portion may be used in later iterations of the overall method <NUM> of training.

At block <NUM>, the discriminator <NUM> is fixed while the semantic parser <NUM> is open to updates. In other words, in some embodiments, the discriminator <NUM> will remain static for the time being to enable the semantic parser <NUM> to be trained based on the current state of the discriminator <NUM>.

In some embodiments, block <NUM> and block <NUM> are performed in parallel. At block <NUM>, the data-input subsystem <NUM> of the training system <NUM> inputs to the semantic parser <NUM> utterances <NUM> from the second portion of the seed data <NUM> that was selected at block <NUM>, and the semantic parser <NUM> generates logical forms <NUM> based on such utterances <NUM>. Early in the training, the semantic parser's output of logical forms <NUM> may be poor. For instance, the logical forms <NUM> generated may be random (e.g., a random arrangement of words and symbols). However, as training proceeds throughout the round of training or over multiple rounds, the semantic parser <NUM> may improve.

At block <NUM>, which may be performed in parallel with block <NUM>, the data-input subsystem <NUM> of training system <NUM> may input to the discriminator <NUM> a stream of tuples, and the discriminator <NUM> may make a prediction of authenticity for each tuple in the stream. Each tuple in the stream of tuples may include an utterance <NUM> and a corresponding logical form <NUM>. More specifically, each tuple received may include an utterance <NUM> from a respective seed tuple selected from the second portion of the seed data <NUM>, as selected at block <NUM>, along with the generated logical form <NUM> output by the semantic parser <NUM>, at block <NUM>, based on that utterance <NUM> being provided as input to the semantic parser <NUM>. For each tuple received, the discriminator <NUM> may classify the tuple based on deemed authenticity. Specifically, the discriminator <NUM> may output a score indicating a likelihood of authenticity; for instance, the score determined by the discriminator <NUM> for a tuple may be a probability that the logical form <NUM> is an authentic and thus accurate representation of the corresponding utterance <NUM> in the tuple or, in other words, the probability that the tuple is a seed tuple from the seed data <NUM>. At this point, the discriminator <NUM> has received some training, as described with reference to <FIG>, so while its output may still need improvement, the discriminator <NUM> may continue to improve during later rounds of training.

At block <NUM>, the error-minimization module <NUM> of the training system <NUM> generates a training signal to train the semantic parser <NUM> via backpropagation, based on the outputs of the semantic parser <NUM> and the discriminator <NUM>, as determined at block <NUM> and block <NUM>. To this end, for instance, the error-minimization module <NUM> applies a second objective function <NUM> that determines a divergence between a selected distribution of predictions form the discriminator <NUM> and the actual distribution of predictions from the discriminator <NUM>. The second objective function <NUM> may, but need not, be the same as the first objective function <NUM> used in training the discriminator <NUM>. In some embodiments, because the training system <NUM> seeks to improve the semantic parser <NUM> to enable the semantic parser <NUM> to fool the discriminator <NUM> into predicting that the outputs of the semantic parser <NUM> are all authentic, a selected distribution of predictions is a series of ones. In some embodiments, such a distribution of predictions would indicate that all logical forms <NUM> generated by the semantic parser <NUM> are authentic. Thus, the second objective function <NUM> may compare this selected distribution (e.g., of a series of ones) to the actual distribution of outputs from the discriminator <NUM> at block <NUM> to determine a degree of difference between these distributions.

The error-minimization module <NUM> may use the second objective function <NUM> to compare this selected distribution to the actual predictions of the discriminator <NUM>, so as to provide a training signal to the semantic parser <NUM>. The training signal represents an error in the predictions made as compared to those that were selected (i.e., desired). Based on the training signal, the semantic parser <NUM> may be updated to reduce the error between the selected distribution and the actual distribution. For instance, the nodes of the neural network acting as the semantic parser <NUM> may be updated based on the training signal.

In some embodiments, as shown in <FIG>, block <NUM> is performed following block <NUM> and block <NUM>. In this case, the error-minimization module <NUM> may provide the training signal only after the semantic parser <NUM> has received all utterances <NUM> in the second portion of the seed data <NUM>. As such, the semantic parser <NUM> is updated based on a batch that includes the entire second portion of the seed data <NUM>. Alternatively, however, block <NUM> may be performed in parallel with block <NUM> and block <NUM>, such that a training signal is sent back to the semantic parser <NUM> while the semantic parser <NUM> is evaluating inputs, such that the semantic parser <NUM> is updated while utterances <NUM> from the second portion of the seed data <NUM> are being provided.

Thus, as described above, a GAN <NUM> can be used to train the semantic parser <NUM> for use in a dialog system <NUM>. Various modifications may be made to the techniques described above, and such modifications are within the scope of this disclosure. For instance, a semi-supervised GAN (SGAN) may be used as the GAN <NUM> to jointly train a supervised discriminator, an unsupervised discriminator, and the semantic parser <NUM>. An SGAN may be particularly useful if the seed data <NUM> is a relatively small set, even for GAN training. Additionally or alternatively, the discriminator <NUM> may incorporate information about a grammar with which logical forms <NUM> must comply. Through reference to this grammar, the discriminator <NUM> may more accurately determine whether a logical form <NUM> is authentic, which may encourage the semantic parser <NUM> to improve more rapidly.

<FIG> is a diagram of a distributed system <NUM> for implementing certain embodiments. In the illustrated embodiment, distributed system <NUM> includes one or more client computing devices <NUM>, <NUM>, <NUM>, and <NUM>, coupled to a server <NUM> via one or more communication networks <NUM>. Clients computing devices <NUM>, <NUM>, <NUM>, and <NUM> may be configured to execute one or more applications.

In various embodiments, server <NUM> may be adapted to run one or more services or software applications that enable the use of backpropagation in a GAN <NUM> to train the semantic parser <NUM> of a dialog system <NUM> as described herein. For instance, server <NUM> may execute some or all aspects of the training system <NUM> or some or all aspects of the dialog system <NUM>.

In certain embodiments, server <NUM> may 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 devices <NUM>, <NUM>, <NUM>, and/or <NUM>. Users operating client computing devices <NUM>, <NUM>, <NUM>, and/or <NUM> may in turn utilize one or more client applications to interact with server <NUM> to utilize the services provided by these components. More specifically, for instance, each of client computing devices <NUM>, <NUM>, <NUM>, and/or <NUM> may be an embedded device configured to execute the dialog system <NUM> and, further, configured to communicate with server <NUM> to enable server <NUM> to train the semantic parser <NUM> of a dialog system <NUM> through backpropagation in a GAN <NUM> as described herein.

In the configuration depicted in <FIG>, server <NUM> may include one or more components <NUM>, <NUM> and <NUM> that implement the functions performed by server <NUM>. 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 system <NUM>. The embodiment shown in <FIG> is thus one example of a distributed system for implementing an embodiment system and is not intended to be limiting.

Users may use client computing devices <NUM>, <NUM>, <NUM>, and/or <NUM> to interact with aspects of the dialog system <NUM> provided by server <NUM> in 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. Although <FIG> depicts 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.

Network(s) <NUM> may be any type of network familiar to those skilled in the art that can support data communications using any of a variety of available protocols, including without limitation TCP/IP (transmission control protocol/Internet protocol), SNA (systems network architecture), IPX (Internet packet exchange), AppleTalk®, and the like. Merely by way of example, network(s) <NUM> can be a local area network (LAN), networks based on Ethernet, Token-Ring, a wide-area network (WAN), the Internet, a virtual network, a virtual private network (VPN), an intranet, an extranet, a public switched telephone network (PSTN), an infrared network, a wireless network (e.g., a network operating under any of the Institute of Electrical and Electronics (IEEE) <NUM> suite of protocols, Bluetooth®, and/or any other wireless protocol), and/or any combination of these and/or other networks.

Server <NUM> may 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. Server <NUM> can 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, server <NUM> may be adapted to run one or more services or software applications that provide the functionality described in the foregoing disclosure.

The computing systems in server <NUM> may run one or more operating systems including any of those discussed above, as well as any commercially available server operating system. Server <NUM> may 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, server <NUM> may include one or more applications to analyze and consolidate data feeds and/or event updates received from users of client computing devices <NUM>, <NUM>, <NUM>, and <NUM>. 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. Server <NUM> may 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 devices <NUM>, <NUM>, <NUM>, and <NUM>.

Distributed system <NUM> may also include one or more data repositories <NUM>, <NUM>. These data repositories may be used to store data and other information in certain embodiments. For example, one or more of data repositories <NUM>, <NUM> may be used to store seed data <NUM> or other data required to train the semantic parser <NUM> of a dialog system <NUM> by backpropagation in a GAN <NUM> as described herein. Data repositories <NUM>, <NUM> may reside in a variety of locations. For example, a data repository used by server <NUM> may be local to server <NUM> or may be remote from server <NUM> and in communication with server <NUM> via a network-based or dedicated connection. Data repositories <NUM>, <NUM> may be of different types. In certain embodiments, a data repository used by server <NUM> may 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 repositories <NUM>, <NUM> may 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 the semantic parser <NUM> of a dialog system <NUM> by backpropagation in a GAN <NUM>, as described herein, may be offered as services via a cloud environment. <FIG> is a block diagram of a cloud-based system environment in which training the semantic parser <NUM>, as described herein, may be offered at least in part as a cloud service, in accordance with certain embodiments. In the embodiment depicted in <FIG>, cloud infrastructure system <NUM> may provide one or more cloud services that may be requested by users using one or more client computing devices <NUM>, <NUM>, and <NUM>. Cloud infrastructure system <NUM> may comprise one or more computers and/or servers that may include those described above for server <NUM>. The computers in cloud infrastructure system <NUM> may be organized as general purpose computers, specialized server computers, server farms, server clusters, or any other appropriate arrangement and/or combination.

Network(s) <NUM> may facilitate communication and exchange of data between client computing devices <NUM>, <NUM>, and <NUM> and cloud infrastructure system <NUM>. Network(s) <NUM> may include one or more networks. The networks may be of the same or different types. Network(s) <NUM> may support one or more communication protocols, including wired and/or wireless protocols, for facilitating the communications.

The embodiment depicted in <FIG> is 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 system <NUM> may have more or fewer components than those depicted in <FIG>, may combine two or more components, or may have a different configuration or arrangement of components. For example, although <FIG> depicts 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 system <NUM>) 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, California, such as middleware services, database services, Java cloud services, and others.

In certain embodiments, cloud infrastructure system <NUM> may 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 system <NUM> may 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 system <NUM>. 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 system <NUM>. Cloud infrastructure system <NUM> then 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 system <NUM> in conversational form. Cloud infrastructure system <NUM> may be configured to provide one or even multiple cloud services.

Cloud infrastructure system <NUM> may provide the cloud services via different deployment models. In a public cloud model, cloud infrastructure system <NUM> may 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 system <NUM> may 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 system <NUM> and 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 devices <NUM>, <NUM>, and <NUM> may be of different types (such as client computing devices <NUM>, <NUM>, <NUM>, and <NUM> depicted in <FIG>) and may be capable of operating one or more client applications. A user may use a client computing device to interact with cloud infrastructure system <NUM>, such as to request a service provided by cloud infrastructure system <NUM>. An attacker may use a client device to send malicious requests.

In some embodiments, the processing performed by cloud infrastructure system <NUM> may 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 system <NUM> for providing training of a semantic parser <NUM> by backpropagation in a GAN <NUM> 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 in <FIG>, cloud infrastructure system <NUM> may include infrastructure resources <NUM> that are utilized for facilitating the provision of various cloud services offered by cloud infrastructure system <NUM>. Infrastructure resources <NUM> may 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 system <NUM> for different customers, the infrastructure resources <NUM> may 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 system <NUM> may itself internally use services <NUM> that are shared by different components of cloud infrastructure system <NUM> and that facilitate the provisioning of services by cloud infrastructure system <NUM>. 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 system <NUM> may comprise multiple subsystems. These subsystems may be implemented in software, or hardware, or combinations thereof. As depicted in <FIG>, the subsystems may include a user interface subsystem <NUM> that enables users or customers of cloud infrastructure system <NUM> to interact with cloud infrastructure system <NUM>. User interface subsystem <NUM> may include various different interfaces such as a web interface <NUM>, an online store interface <NUM> where cloud services provided by cloud infrastructure system <NUM> are advertised and are purchasable by a consumer, and other interfaces <NUM>. For example, a customer may, using a client device, request (service request <NUM>) one or more services provided by cloud infrastructure system <NUM> using one or more of interfaces <NUM>, <NUM>, and <NUM>. For example, a customer may access the online store, browse cloud services offered by cloud infrastructure system <NUM>, and place a subscription order for one or more services offered by cloud infrastructure system <NUM> that 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 in <FIG>, cloud infrastructure system <NUM> may comprise an order management subsystem (OMS) <NUM> that is configured to process the new order. As part of this processing, OMS <NUM> may 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, OMS <NUM> may then invoke an order provisioning subsystem (OPS) <NUM> that 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, OPS <NUM> may 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 system <NUM> may send a response or notification <NUM> to 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 system <NUM> may provide services to multiple customers. For each customer, cloud infrastructure system <NUM> is 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 system <NUM> may 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 system <NUM> may provide services to multiple customers in parallel. Cloud infrastructure system <NUM> may store information for these customers, including possibly proprietary information. In certain embodiments, cloud infrastructure system <NUM> comprises an identity management subsystem (IMS) <NUM> that 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. IMS <NUM> may 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> is a block diagram of an example computer system <NUM> that may be used to implement certain embodiments. For example, in some embodiments, computer system <NUM> may be used to implement any of systems, subsystems, and components described herein. For example, multiple host machines may provide and implement training of the semantic parser <NUM> of a dialog system <NUM> in a GAN <NUM> as described herein. Computer systems such as computer system <NUM> may be used as host machines. As shown in <FIG>, computer system <NUM> includes various subsystems including a processing subsystem <NUM> that communicates with a number of other subsystems via a bus subsystem <NUM>. These other subsystems may include a processing acceleration unit <NUM>, an I/O subsystem <NUM>, a storage subsystem <NUM>, and a communications subsystem <NUM>. Storage subsystem <NUM> may include non-transitory computer-readable storage media including storage media <NUM> and a system memory <NUM>.

Although bus subsystem <NUM> is shown schematically as a single bus, alternative embodiments of the bus subsystem may utilize multiple buses. Bus subsystem <NUM> may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, a local bus using any of a variety of bus architectures, and the like. For example, such architectures may include an Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus, which can be implemented as a Mezzanine bus manufactured to the IEEE P1386. <NUM> standard, and the like.

Processing subsystem <NUM> controls the operation of computer system <NUM> and 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 system <NUM> can be organized into one or more processing units <NUM>, <NUM>, 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 subsystem <NUM> can 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 subsystem <NUM> can 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 subsystem <NUM> can execute instructions stored in system memory <NUM> or on computer-readable storage media <NUM>. 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 memory <NUM> and/or on computer-readable storage media <NUM> including potentially on one or more storage devices. Through suitable programming, processing subsystem <NUM> can provide various functionalities described above. In instances where computer system <NUM> is executing one or more virtual machines, one or more processing units may be allocated to each virtual machine.

In certain embodiments, a processing acceleration unit <NUM> may optionally be provided for performing customized processing or for off-loading some of the processing performed by processing subsystem <NUM> so as to accelerate the overall processing performed by computer system <NUM>.

I/O subsystem <NUM> may include devices and mechanisms for inputting information to computer system <NUM> and/or for outputting information from or via computer system <NUM>. In general, use of the term input device is intended to include all possible types of devices and mechanisms for inputting information to computer system <NUM>. User interface input devices may include, for example, a keyboard, pointing devices such as a mouse or trackball, a touchpad or touch screen incorporated into a display, a scroll wheel, a click wheel, a dial, a button, a switch, a keypad, audio input devices with voice command recognition systems, microphones, and other types of input devices. User interface input devices may also include motion sensing and/or gesture recognition devices such as the Microsoft Kinect® motion sensor that enables users to control and interact with an input device, the Microsoft Xbox® <NUM> game controller, devices that provide an interface for receiving input using gestures and spoken commands. User interface input devices may also include eye gesture recognition devices such as the Google Glass® blink detector that detects eye activity (e.g., "blinking" while taking pictures and/or making a menu selection) from users and transforms the eye gestures as inputs to an input device (e.g., Google Glass®). Additionally, user interface input devices may include voice recognition sensing devices that enable users to interact with voice recognition systems (e.g., Siri® navigator) through voice commands.

Other examples of user interface input devices include, without limitation, three dimensional (3D) mice, joysticks or pointing sticks, gamepads and graphic tablets, and audio/visual devices such as speakers, digital cameras, digital camcorders, portable media players, webcams, image scanners, fingerprint scanners, barcode reader 3D scanners, 3D printers, laser rangefinders, and eye gaze tracking devices. Additionally, user interface input devices may include, for example, medical imaging input devices such as computed tomography, magnetic resonance imaging, position emission tomography, and medical ultrasonography devices. User interface input devices may also include, for example, audio input devices such as MIDI keyboards, digital musical instruments and the like.

In general, use of the term output device is intended to include all possible types of devices and mechanisms for outputting information from computer system <NUM> to a user or other computer. User interface output devices may include a display subsystem, indicator lights, or non-visual displays such as audio output devices, etc. The display subsystem may be a cathode ray tube (CRT), a flat-panel device, such as that using a liquid crystal display (LCD) or plasma display, a projection device, a touch screen, and the like. For example, user interface output devices may include, without limitation, a variety of display devices that visually convey text, graphics and audio/video information such as monitors, printers, speakers, headphones, automotive navigation systems, plotters, voice output devices, and modems.

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

Storage subsystem <NUM> may include one or more non-transitory memory devices, including volatile and non-volatile memory devices. As shown in <FIG>, storage subsystem <NUM> includes a system memory <NUM> and a computer-readable storage media <NUM>. System memory <NUM> may 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 system <NUM>, 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 subsystem <NUM>. In some implementations, system memory <NUM> may 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 in <FIG>, system memory <NUM> may load application programs <NUM> that are being executed, which may include various applications such as Web browsers, mid-tier applications, relational database management systems (RDBMS), etc., program data <NUM>, and an operating system <NUM>. By way of example, operating system <NUM> may 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 the semantic parser <NUM> of a dialog system <NUM> in a GAN <NUM>, as described herein, may be executed in system memory <NUM>.

Computer-readable storage media <NUM> may store programming and data constructs that provide the functionality of some embodiments. Computer-readable storage media <NUM> may provide storage of computer-readable instructions, data structures, program modules, and other data for computer system <NUM>. Software (programs, code modules, instructions) that, when executed by processing subsystem <NUM> provides the functionality described above, may be stored in storage subsystem <NUM>. By way of example, computer-readable storage media <NUM> may include non-volatile memory such as a hard disk drive, a magnetic disk drive, an optical disk drive such as a CD ROM, DVD, a Blu-Ray® disk, or other optical media. Computer-readable storage media <NUM> may include, but is not limited to, Zip® drives, flash memory cards, universal serial bus (USB) flash drives, secure digital (SD) cards, DVD disks, digital video tape, and the like. Computer-readable storage media <NUM> may also include, solid-state drives (SSD) based on non-volatile memory such as flash-memory based SSDs, enterprise flash drives, solid state ROM, and the like, SSDs based on volatile memory such as solid state RAM, dynamic RAM, static RAM, DRAM-based SSDs, magnetoresistive RAM (MRAM) SSDs, and hybrid SSDs that use a combination of DRAM and flash memory based SSDs.

In certain embodiments, storage subsystem <NUM> may also include a computer-readable storage media reader <NUM> that can further be connected to computer-readable storage media <NUM>. Reader <NUM> may receive and be configured to read data from a memory device such as a disk, a flash drive, etc..

In certain embodiments, computer system <NUM> may support virtualization technologies, including but not limited to virtualization of processing and memory resources. For example, computer system <NUM> may provide support for executing one or more virtual machines. In certain embodiments, computer system <NUM> may 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 system <NUM>. Accordingly, multiple operating systems may potentially be run concurrently by computer system <NUM>.

Communications subsystem <NUM> provides an interface to other computer systems and networks. Communications subsystem <NUM> serves as an interface for receiving data from and transmitting data to other systems from computer system <NUM>. For example, communications subsystem <NUM> may enable computer system <NUM> to 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 subsystem <NUM> may support both wired and/or wireless communication protocols. For example, in certain embodiments, communications subsystem <NUM> may 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 <NUM>, <NUM> or EDGE (enhanced data rates for global evolution), WiFi (IEEE <NUM>. 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 subsystem <NUM> can provide wired network connectivity (e.g., Ethernet) in addition to or instead of a wireless interface.

Communication subsystem <NUM> can receive and transmit data in various forms. For example, in some embodiments, in addition to other forms, communications subsystem <NUM> may receive input communications in the form of structured and/or unstructured data feeds <NUM>, event streams <NUM>, event updates <NUM>, and the like. For example, communications subsystem <NUM> may be configured to receive (or send) data feeds <NUM> in 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.

In certain embodiments, communications subsystem <NUM> may be configured to receive data in the form of continuous data streams, which may include event streams <NUM> of real-time events and/or event updates <NUM>, that may be continuous or unbounded in nature with no explicit end. Examples of applications that generate continuous data may include, for example, 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.

Communications subsystem <NUM> may also be configured to communicate data from computer system <NUM> to other computer systems or networks. The data may be communicated in various different forms such as structured and/or unstructured data feeds <NUM>, event streams <NUM>, event updates <NUM>, and the like to one or more databases that may be in communication with one or more streaming data source computers coupled to computer system <NUM>.

Computer system <NUM> can 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 system <NUM> depicted in <FIG> is intended only as a specific example. Many other configurations having more or fewer components than the system depicted in <FIG> are 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.

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.

Where devices, systems, components or modules are described as being configured to perform certain operations or functions, such configuration can be accomplished, for example, by designing electronic circuits to perform the operation, by programming programmable electronic circuits (such as microprocessors) to perform the operation such as by executing computer instructions or code, or processors or cores programmed to execute code or instructions stored on a non-transitory memory medium, or any combination thereof. Processes can communicate using a variety of techniques including but not limited to conventional techniques for inter-process communications, and different pairs of processes may use different techniques, or the same pair of processes may use different techniques at different times.

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.

Claim 1:
A method for training a semantic parser (<NUM>) for use in a dialog system (<NUM>), the method comprising:
accessing (<NUM>) seed data (<NUM>) comprising seed tuples, each seed tuple of the seed data (<NUM>) comprising a respective seed utterance (<NUM>) and a respective seed logical form (<NUM>) corresponding to the respective seed utterance (<NUM>); and
training (<NUM>) a semantic parser (<NUM>) and a discriminator (<NUM>) in a generative adversarial network, GAN (<NUM>), wherein the training (<NUM>) comprises:
inputting random utterances (<NUM>) to the semantic parser (<NUM>), wherein each random utterance (<NUM>) is a randomly generated utterance or a randomly selected utterance from a source other than the seed data (<NUM>);
generating (<NUM>), by the semantic parser, generated logical forms (<NUM>) based on the random utterances (<NUM>);
inputting, to the discriminator (<NUM>), tuples comprising generated tuples and a subset of seed tuples selected from the seed data (<NUM>), wherein the generated tuples comprise the random utterances (<NUM>) and the generated logical forms (<NUM>);
determining (<NUM>), by the discriminator (<NUM>), predicted authenticities of the tuples;
comparing the predicted authenticities to actual authenticities of the tuples;
updating the discriminator (<NUM>) based on comparing the predicted authenticities to the actual authenticities of the tuples;
inputting seed utterances (<NUM>) to the semantic parser (<NUM>), wherein the seed utterances (<NUM>) are selected (<NUM>) from the seed tuples;
generating (<NUM>), by the semantic parser (<NUM>), additional generated logical forms (<NUM>) based on the seed utterances (<NUM>) for each selected seed utterance (<NUM>);
inputting, to the discriminator (<NUM>), additional generated tuples comprising the seed utterances (<NUM>) from the seed data (<NUM>) and the additional generated logical forms (<NUM>);
determining (<NUM>), by the discriminator (<NUM>), additional predicted authenticities of the additional generated tuples;
comparing the additional predicted authenticities to selected authenticities of the additional generated tuples; and
updating the semantic parser (<NUM>) based on comparing the additional predicted authenticities to the selected authenticities of the additional generated tuples, such that the semantic parser (<NUM>) learns to map utterances (<NUM>) to logical forms (<NUM>) and the discriminator (<NUM>) learns to recognize authentic logical forms (<NUM>).