Patent Publication Number: US-11023683-B2

Title: Out-of-domain sentence detection

Description:
BACKGROUND 
     The present invention relates to out-of-domain sentence detection, and more specifically, to training an out-of-domain sentence detector. 
     SUMMARY 
     According to an embodiment of the present disclosure, a computer-implemented method for training an out-of-domain sentence detector includes obtaining a training data set including text data indicating one or more phrases or sentences. The computer-implemented method includes training a classifier using supervised machine learning based on the training data set and additional text data indicating one or more out-of-domain phrases or sentences. The computer-implemented method includes training an autoencoder using unsupervised machine learning based on the training data. The computer-implemented method further includes combining the classifier and the autoencoder to generate the out-of-domain sentence detector configured to generate an output indicating a classification of whether input text data corresponds to an out-of-domain sentence. The output is based on a combination of a first output of the classifier and a second output of the autoencoder. 
     According to an embodiment of the present disclosure, an apparatus includes a processor and a memory coupled to the processor and storing instructions that, when executed by the processor, cause the processor to perform operations including obtaining a training data set including text data indicating one or more phrases or sentences. The operations include training a classifier using supervised machine learning based on the training data set and additional text data indicating one or more out-of-domain phrases or sentences. The operations include training an autoencoder using unsupervised machine learning based on the training data. The operations include combining the classifier and the autoencoder to generate an out-of-domain sentence detector configured to generate an output indicating a classification of whether input text data corresponds to an out-of-domain sentence. The output is based on a combination of a first output of the classifier and a second output of the autoencoder. 
     According to an embodiment of the present disclosure, a computer program product for training an out-of-domain sentence detector includes a computer readable storage medium having program instructions embodied therewith. The program instructions are executable by a processor to cause the processor to perform operations including obtaining a training data set including text data indicating one or more phrases or sentences. The operations include training a classifier using supervised machine learning based on the training data set and additional text data indicating one or more out-of-domain phrases or sentences. The operations include training an autoencoder using unsupervised machine learning based on the training data. The operations further include combining the classifier and the autoencoder to generate the out-of-domain sentence detector configured to generate an output indicating a classification of whether input text data corresponds to an out-of-domain sentence. The output is based on a combination of a first output of the classifier and a second output of the autoencoder. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram of a system that is operable to train an out-of-domain sentence detector; 
         FIGS. 2A-2B  are examples of selecting sentences for use as additional text data in training the classifier included in the out-of-domain sentence detector of  FIG. 1 ; 
         FIG. 3  is a diagram of an example of building an autoencoder included in the out-of-domain sentence detector of  FIG. 1 ; 
         FIG. 4  is a block diagram of a computing device configured to train an out-of-domain sentence detector; 
         FIG. 5  is a flowchart of a method for training an out-of-domain sentence detector; 
         FIG. 6  is a flowchart that illustrates an example of a method of deploying an out-of-domain sentence detector; 
         FIG. 7  is a flowchart that illustrates an example of using an out-of-domain sentence detector in an on demand context according to an implementation of the present disclosure; 
         FIG. 8  depicts a cloud computing environment according to an implementation of the present disclosure; and 
         FIG. 9  depicts abstraction model layers according to an implementation of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     Particular implementations are described with reference to the drawings. In the description, common features are designated by common reference numbers throughout the drawings. As used herein, various terminology is used for the purpose of describing particular implementations only and is not intended to be limiting. For example, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. Further, the terms “comprise,” “comprises,” and “comprising” may be used interchangeably with “include,” “includes,” or “including.” Additionally, it will be understood that the term “wherein” may be used interchangeably with “where.” As used herein, “exemplary” may indicate an example, an implementation, and/or an aspect, and should not be construed as limiting or as indicating a preference or a preferred implementation. As used herein, an ordinal term (e.g., “first,” “second,” “third,” etc.) used to modify an element, such as a structure, a component, an operation, etc., does not by itself indicate any priority or order of the element with respect to another element, but rather merely distinguishes the element from another element having a same name (but for use of the ordinal term). As used herein, the term “set” refers to a grouping of one or more elements, and the term “plurality” refers to multiple elements. 
     In the present disclosure, terms such as “determining”, “calculating”, “generating”, “adjusting”, “modifying”, etc. may be used to describe how one or more operations are performed. It should be noted that such terms are not to be construed as limiting and other techniques may be utilized to perform similar operations. Additionally, as referred to herein, “generating”, “calculating”, “using”, “selecting”, “accessing”, and “determining” may be used interchangeably. For example, “generating”, “calculating”, or “determining” a parameter (or a signal) may refer to actively generating, calculating, or determining the parameter (or the signal) or may refer to using, selecting, or accessing the parameter (or signal) that is already generated, such as by another component or device. Additionally, “adjusting” and “modifying” may be used interchangeably. For example, “adjusting” or “modifying” a parameter may refer to changing the parameter from a first value to a second value (a “modified value” or an “adjusted value”). As used herein, “coupled” may include “communicatively coupled,” “electrically coupled,” or “physically coupled,” and may also (or alternatively) include any combinations thereof. Two devices (or components) may be coupled (e.g., communicatively coupled, electrically coupled, or physically coupled) directly or indirectly via one or more other devices, components, wires, buses, networks (e.g., a wired network, a wireless network, or a combination thereof), etc. Two devices (or components) that are electrically coupled may be included in the same device or in different devices and may be connected via electronics, one or more connectors, or inductive coupling, as illustrative, non-limiting examples. In some implementations, two devices (or components) that are communicatively coupled, such as in electrical communication, may send and receive electrical signals (digital signals or analog signals) directly or indirectly, such as via one or more wires, buses, networks, etc. As used herein, “directly coupled” may include two devices that are coupled (e.g., communicatively coupled, electrically coupled, or physically coupled) without intervening components. 
     As dialogue systems become more popular as a cloud artificial intelligence (AI) service, a challenge becomes identifying requests that are out-of-domain, and may result in unpredictable responses. For example, the domain refers to the type of expected questions or requests for a particular dialogue system (e.g., questions or requests that have to do with the purpose of the dialogue system). To further illustrate, a cloud dialogue service that is designed to provide weather-related information would not expect to receive a question about who is the director of a movie (e.g., this is an out-of-domain question). The present disclosure describes systems, apparatus, methods, and computer program products for training an out-of-domain sentence detector. The out-of-domain sentence detector of the present disclosure combines (e.g., is an ensemble approach of) two different machine learning models: a classifier and an autoencoder, to provide a more robust and accurate out-of-domain sentence detector. 
     To illustrate, the out-of-domain sentence detector of the present disclosure includes a classifier that is trained using a training data set of in-domain training examples and additional text data including out-of-domain examples. The training data set includes in-domain sentences or phrases that are provided by a customer. The additional text data can include out-of-domain sentences or phrases that are provided by a customer, in addition to out-of-domain sentences or phrases that are retrieved from an example sentence pool (e.g., an external corpus) that is accessible to the classifier. In some implementations, the training process disclosed herein provides relatively the same number of in-domain training examples as out-of-domain training examples. If fewer than a target number of out-of-domain training examples are provided (or if no out-of-domain training examples are provided), the out-of-domain training examples are retrieved from the example sentence pool. For example, the example sentences in the pool may be clustered into clusters in a feature space, and based on a distance between a training example and sentences in the clusters, one or more sentences from the pool may be selected as out-of-domain training examples. As a first example, an out-of-domain example sentence provided by the customer may be mapped into the feature space, and if a distance between the out-of-domain example sentence and a particular example sentence from the pool fails to satisfy the threshold (e.g., the particular example sentence is sufficiently similar to the out-of-domain example sentence), the particular example sentence is selected as an out-of-domain example sentence. As a second example, an in-domain example sentence provided by the customer may be mapped into the feature space, and if a distance between the in-domain example sentence and a particular example sentence from the pool satisfies a second threshold (e.g., the particular example sentence is sufficiently dissimilar to the in-domain example sentence), the particular example sentence is selected as an out-of-domain example sentence. Thus, regardless of whether the training data set includes out-of-domain example sentences, out-of-domain example sentences can be retrieved from the example sentence pool for use in training the classifier. 
     The out-of-domain sentence detector of the present disclosure also includes an autoencoder. The autoencoder is trained using unsupervised learning based on the in-domain training examples of the training data set. Because in-domain sentences have a common distribution, the autoencoder is able to learn an encoding of an in-domain sentence. Further, a reconstruction error output by the autoencoder can be used to indicate whether the input text is an in-domain sentence (or an out-of-domain sentence). A forcing function, such as a sigmoid function, may be applied to the reconstruction error to generate an output that is combined with an output of the classifier. For example, the combination may be an average or a weighted average of the two outputs, or a result of a voting process (e.g., if either or both outputs indicate out-of-domain, the result may be classified as an out-of-domain sentence (or vice versa)). The output (e.g., a classification) indicates a classification of whether input text data provided to the out-of-domain sentence classifier is an out-of-domain sentence (or an in-domain sentence). The classification is used to determine a next action for a system to perform. For example, if the classification indicates an in-domain sentence, the in-domain sentence is sent for further processing, such as intent detection, in order to generate a response. If the classification indicates an out-of-domain sentence, the system may issue a prompt to a user indicating that their request is outside the scope of the system. 
     One advantage provided by the systems, methods, and computer program products described herein is the generation and training of an out-of-domain sentence detector that is more robust and more accurate than other out-of-domain sentence detectors. For example, by combining the outputs of the classifier and the autoencoder, the out-of-domain sentence detector of the present disclosure may be more accurate in situations where the out-of-domain sentences are too similar to the in-domain sentences, which may cause difficulties for the classifier, and in situations where one or more in-domain sentences are different than the other in-domain sentences, which may cause difficulties for the autoencoder. 
     With reference to  FIG. 1 , a system  100  for training an out-of-domain sentence detector  102  is shown. In the illustrated example, the out-of-domain sentence detector  102  includes a classifier  104 , an autoencoder  106 , a forcing function  120 , and a combiner  108 . The classifier  104  is coupled to the combiner  108 . The autoencoder  106  is coupled to the forcing function  120 . The forcing function  120  is coupled to the combiner  108 . 
     In a particular implementation, each of the elements  102 - 108  and  120  corresponds to hardware. For example, the elements  102 - 108  and  120  may be embodied in a processor, a controller, a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), or another form of hardware. In other implementations, the operations described with reference to the elements  102 - 108  and  120  are performed by a processor executing computer-readable instructions, as further described with reference to  FIG. 4 . 
     The system  100  is configured to obtain a training data set  110 . The training data set  110  includes a plurality of training examples. Each training example includes text data of a respective phrase or sentence. Each training example also includes a class (e.g., domain) label for the phrase or sentence. For example, each training example is labeled as either in-domain or out-of-domain. In some implementations, the training data set  110  is provided to the system  100 , such as being stored on a memory accessible (or included in) the system  100  or being received from another device via network transmission. In a particular implementation, the training data set  110  has already been stored as text data. Alternatively, audio data may be provided, and automatic speech recognition and text to speech conversion may be performed on the audio data to generate the training data set  110 . 
     The training examples of the training data set  110  are generated by an end-user for the out-of-domain sentence detector  102 , such as a customer of a producer of the system  100 . The training examples may correspond to examples of speech, such as questions, commands, etc., that are expected to be received from users of a voice response system that integrates the out-of-domain sentence detector  102 . The out-of-domain sentence detector  102  is trained based on the training data set  110  to detect inputs to the response system that are outside the scope of what the response system is designed to handle. For example, if the response system is a weather response system, a query such as “what is today&#39;s high temperature” would be considered in-domain, while a query such as “what is the capital of Montana” would be considered out-of-domain. Out-of-domain requests are handled differently than in-domain requests, as further described herein, to prevent a user from receiving a response that is outside of the user&#39;s expectations. 
     The training data set  110  may include as few as five to ten training examples, or as many as millions of training examples, depending on the customer and how much information is known ahead of time. Each of the training examples is labeled to indicate whether the respective training example is in-domain or out-of-domain, for use in supervised learning, as further described herein. The training examples may also include text from one or multiple languages. 
     The training data set  110  is used to train the classifier  104 . To illustrate, the training data set  110  is provided to the classifier  104  to train the classifier  104  to classify input text as an in-domain sentence or as an out-of-domain sentence. In a particular implementation, the classifier  104  is a binary classifier that is configured to output a first value (indicating a classification as in-domain) or a second value (indicating a classification as out-of-domain). 
     In order to train the classifier  104 , training data provided to the classifier  104  should include both examples of in-domain sentences (or phrases) and out-of-domain sentences (or phrases), so that the classifier  104  can learn, through supervised learning, the boundary between an in-domain sentence and an out-of-domain sentence. To efficiently train the classifier  104 , the number of in-domain training examples should be substantially equal to the number of out-of-domain training examples. However, if a customer provides out-of-domain training examples at all, it is likely the customer does not provide as many out-of-domain training examples as in-domain training examples. Thus, the system  100  is configured to determine additional text data  114  to provide to the classifier  104  along with the training data set  110  for training the classifier  104 . The additional text data  114  includes out-of-domain training examples. 
     In a particular implementation, some of the additional text data  114  (e.g., the out-of-domain training examples) is received from the customer. Additionally, a portion of the additional text data  114  (or an entirety, if no out-of-domain training examples are received from the customer) is obtained from an example sentence pool  112 . The example sentence pool  112  includes a corpus of example sentences (or phrases). In a particular implementation, the example sentence pool  112  is stored at a memory of the system  100 . In an alternate implementation, the example sentence pool  112  is stored externally and is accessible to the system  100 . 
     In a particular implementation, the example sentences in the example sentence pool  112  are clustered into a plurality of clusters in a feature space. The clustering may be performed using any clustering technique, such as a K-Means, DBSCAN, or other clustering techniques. The size of the clusters may be set as a hyperparameter to control the performance, efficiency, and memory usage of the system  100 . In this implementation, example sentences from the example sentence pool  112  are selected for inclusion in the additional text data  114  based on a distance in the feature space between a training example and the example sentence. 
     As a first example, an out-of-domain training example (e.g., from the training data set  110 ) is used to select a similar sentence from the example sentence pool  112  that is to be used as an out-of-domain training example. To illustrate, the out-of-domain training example is mapped into the feature space of the example sentence pool  112 . A distance in the feature space between the out-of-domain training example and an example sentence of the nearest cluster is determined. If the distance fails to satisfy a first threshold (e.g., is less than the first threshold), the example sentence is selected for inclusion in the additional text data  114 . In a particular implementation, the distance is a cosine distance. In another particular implementation, the distance is an L 2  distance. In this manner, sentences that are similar to received out-of-domain training examples (e.g., based on distance in the feature space) are selected as additional out-of-domain training examples. This example is further described with reference to  FIG. 2A . 
     As a second example, an in-domain training example (e.g., from the training data set  110 ) is used to select a dissimilar sentence from the example sentence pool  112  that is to be used as an out-of-domain training example. To illustrate, the in-domain training example is mapped into the feature space of the example sentence pool  112 . A distance in the feature space between the in-domain training example and an example sentence of the farthest cluster is determined. If the distance satisfies a second threshold (e.g., is greater than or equal to the second threshold), the example sentence is selected for inclusion in the additional text data  114 . The distance may be a cosine distance, an L 2  distance, or another type of distance. In this manner, sentences that are sufficiently dissimilar to received in-domain training examples (e.g., based on distance in the feature space) are selected as out-of-domain training examples. This example is further described with reference to  FIG. 2B . 
     A number of example sentences are obtained from the example sentence pool  112  such that the number of in-domain examples and out-of-domain examples are substantially equal. For example, if twenty in-domain sentence examples are included in the training data set  110  and two out-of-domain example sentences are also included in the training data set  110  (or otherwise received from the customer), eighteen out-of-domain example sentences are retrieved from the example sentence pool  112  as the additional text data  114 . After obtaining the additional text data  114  (e.g., the out-of-domain example sentences), the training data set  110  and the additional text data  114  are used to train the classifier  104  to classify input text as either in-domain or out-of-domain. An output of the classifier  104  is combined with an output of the autoencoder by the combiner  108 , as further described herein. 
     The training data set  110  is also used to train the autoencoder  106 . The autoencoder  106  is configured to learn, using unsupervised learning, the distribution of in-domain data. To illustrate, the in-domain training examples of the training data set  110  (without the labels) are provided to the autoencoder  106  to train the autoencoder  106  to learn a representation (e.g., an encoding) of the in-domain training examples in an unsupervised manner. Along with learning the encoding, the autoencoder  106  is also configured to generate a reconstruction from a reduced representation that is as close as possible to the original input. In a particular implementation, proving the training data set  110  to the autoencoder  106  includes generating one or more embedding vectors based on the training data set  110  and providing the one or more embedding vectors to the autoencoder  106 , as further described with reference to  FIG. 3 . 
     A reconstruction error  118  output by the autoencoder  106  indicates how well the autoencoder  106  has performed at reconstruction, and can be used to indicate whether the original input is in-domain or out-of-domain. For example, if the original input is in-domain, the reconstruction error  118  should be relatively small. If the original input is out-of-domain, the reconstruction error  118  should be relatively large (e.g., satisfy a threshold). Thus, the reconstruction error  118  output by the autoencoder  106  can be used to indicate whether input text is in-domain or out-of-domain. 
     The forcing function  120  is applied to the reconstruction error  118  to generate an output for combination with the output of the classifier  104 . For example, the forcing function  120  may be applied to the reconstruction error  118  to generate a value that can be more easily combined with the output of the classifier  104  (e.g., a binary digit). In a particular implementation, the forcing function  120  includes a sigmoid function to force the reconstruction error  118  to be a binary digit. 
     The combiner  108  is configured to combine outputs of the classifier  104  and the autoencoder  106  to generate a combined output that represents a classification of whether input text is in-domain or out-of-domain. Thus, the combiner  108  effectively combines the classifier  104  and the autoencoder  106  to form the out-of-domain sentence detector  102 . In a particular implementation, the combiner  108  is configured to perform an average of the output of the classifier  104  and a value based on the output of the autoencoder  106  (e.g., after application of the forcing function  120 ). In another particular implementation, the combiner  108  is configured to perform a weighted average of the output of the classifier  104  and a value based on the output of the autoencoder  106  (e.g., after application of the forcing function  120 ). The weights applied to the outputs may depend on a target reliance rate for each of the classifier  104  or the autoencoder  106 . In some implementations, the weights may be based on the training data set  110 , which may indicate situations in which either the classifier  104  or the autoencoder  106  is more likely to be accurate. For example, if there are multiple out-of-domain training examples included in the training data set  110 , the weighting for the classifier  104  may be increased, while if there are a large number of in-domain training examples in the training data set  110 , the weighting for the autoencoder  106  may be increased. In another particular implementation, the combiner  108  is configured to combine the outputs using a voting function. For example, if either or both of the outputs indicate an out-of-domain sentence, the final output is classified as an out-of-domain sentence. Alternatively, in another example, if either or both of the outputs indicate an in-domain sentence, the final output is classified as an in-domain sentence. 
     During operation, the out-of-domain sentence detector  102  may be operated in a training mode or in a use mode. During operation in the training mode, the out-of-domain sentence detector  102  is trained by providing the training data set  110  to the classifier  104  and to the autoencoder  106 . The additional text data  114  is also used to train the classifier  104 . In a particular implementation, the training data set  110  and at least a portion of the additional text data  114  are obtained from a customer (e.g., the customer provides in-domain training examples and out-of-domain training examples). Additionally, at least a portion of the additional text data  114  may be obtained from the example sentence pool  112 . For example, based on distances in a feature space between clusters of sentences in the example sentence pool  112  and training examples (either in-domain or out-of-domain), sentences for the example sentence pool  112  may be selected for inclusion in the additional text data  114 , as further described with reference to  FIGS. 2A and 2B . In a particular implementation, the number of out-of-domain training examples selected for inclusion in the additional text data  114  is the same as the number of the in-domain training examples included in the training data set  110 . 
     After training the out-of-domain sentence detector  102  (e.g., after training the classifier  104  and the autoencoder  106 ), the out-of-domain sentence detector  102  is operated in a use mode to classify received input text. For example, the out-of-domain sentence detector  102  receives input text data  116 , such as from a user interface or as a result of automatic speech recognition. The input text data  116  is provided to the classifier  104  and to the autoencoder  106 . The classifier  104  generates a first output  122  that indicates whether the input text data  116  is an in-domain sentence (or phrase) or an out-of-domain sentence (or phrase). The autoencoder  106  generates a representation of the input text data  116  and reconstructs the representation. As a result, the autoencoder  106  outputs the reconstruction error  118 . If the reconstruction error  118  satisfies a threshold, the reconstruction error  118  indicates that the input text data  116  is an out-of-domain sentence (or phrase), and if the reconstruction error  118  fails to satisfy the threshold, the reconstruction error  118  indicates that the input text data  116  is an in-domain sentence. 
     The forcing function  120 , such as a sigmoid function or other activation function, is applied to the reconstruction error  118  to generate a second output  124  that indicates a classification of the input text data  116  by the autoencoder  106 . The combiner  108  combines the first output  122  with the second output  124  to generate a classification  126  that indicates whether the input text data  116  is an in-domain sentence (or phrase) or an out-of-domain sentence (or phrase). For example, the combiner  108  may average, or perform a weighted average, on the first output  122  and the second output  124 , to generate the classification  126 . Based on the classification  126 , a system executing the out-of-domain sentence detector  102  may perform one or more operations. For example, if the classification  126  indicates that the input text data  116  is an in-domain sentence (or phrase), the input text data  116  may be provided to an intent classifier to process the input text data  116  and to determine a response to the input text data  116 . If the classification  126  indicates that the input text data  116  is an out-of-domain sentence (or phrase), the system may issue a prompt advising the user to input a more on-topic request, instead of processing the input text data  116  and potentially providing a response that does not make sense to the user. 
     The out-of-domain sentence detector  102  may be stored at a memory (for execution by the system  100 ) or transmitted to another device for use by the other device. In a particular implementation, the out-of-domain sentence detector  102  may be deployed as part of a virtual “chat-bot” that enables users to ask questions from their computer and receive answers based on the text of the questions. For example, the chat-bot may display a support prompt, and a user may enter text or voice commands in response to the support prompt. In this implementation, the out-of-domain sentence detector  102  is software (or a part of software) that may be executed at the system  100  or another device to determine whether text input is out-of-domain or in-domain, such that appropriate actions may be taken. Thus, at a high-level, the system  100  is configured to generate software. 
     One advantage provided by system  100  is the generation and training of the out-of-domain sentence detector  102  that is more robust and more accurate than other out-of-domain sentence detectors. For example, by combining the outputs of the classifier  104  and the autoencoder  106 , the out-of-domain sentence detector  102  may be more accurate in situations where the out-of-domain sentences are too similar to the in-domain sentences, which may cause difficulties for the classifier  104 , and in situations where one or more in-domain sentences are different than the other in-domain sentences, which may cause difficulties for the autoencoder  106 . 
     Additionally, the out-of-domain sentence detector  102  experiences the benefits of both underlying models, such as the easy interpretability and simple threshold definition of the classifier  104  and the easier training (e.g., without out-of-domain training examples) of the autoencoder  106 . By accurately detecting out-of-domain sentences, proper actions may be taken, such as requesting a user to resubmit a request that is more on-topic, instead of providing a response that is possibly outside of what the user would expect to a given request. 
       FIGS. 2A and 2B  are examples of selecting sentences for use as additional text data, such as the additional text data  114 , in training the classifier  104  in the out-of-domain sentence detector  102  of  FIG. 1 . 
       FIG. 2A  illustrates a first example  200 . In this example, the example sentences of the example sentence pool  112  have been mapped to a feature space. For example, each example sentence is converted to an N-sized feature vector (e.g., by extracting features from the example sentences), and the feature space is an N-dimensional feature space where each dimension corresponds to a feature. Distances in the feature space represent similarity between the example sentences (e.g., an example sentence that is near another example sentence is similar to the example sentence, and an example sentence that is farther away from another example sentence is less similar to the other example sentence). Additionally, a clustering operation has been performed on the example sentences. For example, a K-Means clustering operation, a DBSCAN clustering operation, or another type of clustering operation has been performed. The clustering operation generates clusters of example sentences in the feature space based on the similarity of the various example sentences. As a result, the example sentences have been clustered into seven clusters: cluster C 1 , cluster C 2 , cluster C 3 , cluster C 4 , cluster C 5 , cluster C 6 , and cluster C 7 . Although seven clusters are illustrated in  FIG. 2A , such example is not limiting, and in other examples the example sentences may be clustered into more than seven or fewer than seven clusters. 
     In the example of  FIG. 2A , an out-of-domain example sentence  202  is obtained, such as from a customer. The out-of-domain example sentence  202  is mapped into the feature space. For example, the out-of-domain example sentence  202  has been mapped to a location in the feature space illustrated in  FIG. 2A . If other out-of-domain examples are to be selected from the example sentence pool  112 , the nearest cluster to the out-of-domain example sentence  202  is determined. In the example of  FIG. 2A , the nearest cluster is cluster C 4 . A particular sentence  204  is selected from cluster C 4 , and a distance in the feature space between the particular sentence  204  and the out-of-domain example sentence  202  is determined. For example, a cosine or L 2  distance may be determined. If the distance fails to satisfy (e.g., is less than) a first threshold  206 , then the particular sentence  204  is sufficiently similar to the out-of-domain example sentence  202 , and the particular sentence  204  is identified as an out-of-domain sentence and is selected for inclusion in additional text data  210 . The additional text data  210  may include or correspond to the additional text data  114 . If the distance satisfies the first threshold  206 , then the particular sentence  204  is determined not to be an out-of-domain sentence and is not included in the additional text data  210 . 
       FIG. 2B  illustrates a second example  220 . Similar to the example of  FIG. 2A , the example sentences of the example sentence pool  112  have been mapped to the feature space, and a clustering operation has been performed on the example sentences resulting in the clusters C 1 -C 7 . Although seven clusters are illustrated in  FIG. 2B , such example is not limiting, and in other examples the example sentences may be clustered into more than seven or fewer than seven clusters. 
     In the example of  FIG. 2B , an in-domain example sentence  222  is obtained, such as from a customer. The in-domain example sentence  222  is mapped into the feature space. For example, the in-domain example sentence  222  has been mapped to a location in the feature space illustrated in  FIG. 2B . To select out-of-domain examples from the example sentence pool  112 , the farthest cluster from the in-domain example sentence  222  is determined. In the example of  FIG. 2B , the farthest cluster is cluster C 1 . A particular sentence  224  is selected from cluster C 1 , and a distance in the feature space between the particular sentence  224  and the in-domain example sentence  222  is determined. For example, a cosine or L 2  distance may be determined. If the distance satisfies (e.g., is greater than or equal to) a second threshold  208 , then the particular sentence  224  is sufficiently dissimilar to the in-domain example sentence  222 , and the particular sentence  224  is identified as an out-of-domain sentence and is selected for inclusion in additional text data  210 . If the distance fails to satisfy the second threshold  208 , then the particular sentence  224  is determined not to be an out-of-domain sentence and is not included in the additional text data  210 . 
     Thus,  FIGS. 2A and 2B  illustrate examples of selecting example sentences from the example sentence pool  112  for use as out-of-domain training examples for the classifier  104 .  FIG. 2A  illustrates an example of selecting out-of-domain training examples based on an out-of-domain training sample.  FIG. 2B  illustrates an example of selecting out-of-domain training examples based on an in-domain training example. By selecting a sufficient number of out-of-domain training examples, such that the number of in-domain training examples and the number of out-of-domain training examples are substantially equal, accuracy of the classifier  104  is improved. 
       FIG. 3  illustrates a diagram of an example  300  of building an autoencoder, such as the autoencoder  106  in the out-of-domain sentence detector  102  of  FIG. 1 . In the example of  FIG. 3 , raw text  302 , such as a document, one or more sentences, or one or more sentence fragments, is obtained. Pre-training, such as feature extraction, is performed on the raw text  302 , at  304 . The pre-training is performed at the word-level and generates word representations  306 , such as feature vectors that represent words in the raw text  302 . For example, the word representations  306  include a first word representation w 1 , a second word representation w 2 , and an mth word representation wm, where m can be any positive integer. 
     The word representations  306  and in-domain sentences  308 , including a first sentence s 1 , a second sentence s 2 , and an nth sentence sn, where n can be any positive integer, are used to build an embedder for neural sentence embedding, at  310 . The in-domain sentences  308  may include or correspond to the training data set  110  of  FIG. 1 . In a particular implementation, the embedder includes a long short-term memory (LSTM) network. In another particular implementation, the embedder includes a universal sentence encoder (USE). In another particular implementation, the embedder includes embeddings for language models (ELMo). 
     The embedder is configured to generate embedding vectors based on the in-domain sentences  308  and the word representations  306 . For example, the embedder generates sentence embeddings  312  and embedding vectors  314  (e.g., in-domain sentences represented by the sentence embeddings  312 ). The embedding vectors  314  include a first embedding vector ev 1 , a second embedding vector ev 2 , and an nth embedding vector evn. The embedding vectors  314  are reduced dimensionality representations of the in-domain sentences  308 . For example, the embedding vectors  314  (e.g., the sentence representations) can be visualized as representing points in a feature space (also referred to as an “embedding”), where two points that are near each other in the feature space are more similar to one another than two points that are further away from each other. In a particular implementation, the embedding vectors  314  have values that indicate whether certain words, phrases, sentences, or other language features are present (or detected) in the in-domain sentences  308 . 
     The embedding vectors  314  are used to train an autoencoder, at  316 . For example, the embedding vectors  314  are used to train an encoder  318  and a decoder  320 . The encoder  318  and the decoder  320  make up the autoencoder, such as the autoencoder  106  of  FIG. 1 . The encoder  318  is configured to generate a representation of the embedding vectors, and the decoder  320  is configured to generate a reconstruction of the embedding vectors, as described with reference to  FIG. 1 . After training, a reconstruction error output by the autoencoder (e.g., the encoder  318  and the decoder  320 ) indicates whether input text is an in-domain sentence or an out-of-domain sentence, as described with reference to  FIG. 1 . This task (e.g., identifying objects of a specific class by learning from a training set containing only objects of that class) is often referred to as “one-class classification.” Thus,  FIG. 3  illustrates an example of training an autoencoder (e.g., the encoder  318  and the decoder  320 ) to perform one-class classification (e.g., based on the in-domain sentences  308 ). 
       FIG. 4  illustrates a diagram of a computing device  402  configured to train an out-of-domain sentence detector  426 . The computing device  402  may include or correspond to a desktop computer, a laptop computer, a tablet computer, a server, a mainframe, or any other type of computing device. 
     The computing device  402  includes a processor  404 , a transmitter  406 , a receiver  408 , a user interface  410 , and a memory  420 . The processor  404 , the transmitter  406 , the receiver  408 , the user interface  410 , and the memory  420  may be coupled together via a bus  412  (or other connection). The example illustrated in  FIG. 4  is not intended to be limiting, and in other implementations, one or more of the processor  404 , the transmitter  406 , the receiver  408 , the user interface  410 , the bus  412 , and the memory  420  are optional, or more components may be included in the computing device  402 . 
     The transmitter  406  is configured to enable the computing device  402  to send data to one or more other devices via direct connection or via one or more networks, and the receiver  408  is configured to enable the computing device  402  to receive data from one or more other devices via direct connection or via one or more networks. The one or more networks may include Institute of Electrical and Electronics Engineers (IEEE) 802 wireless networks, Bluetooth networks, telephone networks, optical or radio frequency networks, or other wired or wireless networks. In some implementations, the transmitter  406  and the receiver  408  may be replaced with a transceiver that enables sending and receipt of data from one or more other devices. 
     The user interface  410  is configured to facilitate user interaction. For example, the user interface  410  is adapted to receive input from a user, to provide output to a user, or a combination thereof. In some implementations, the user interface  410  conforms to one or more standard interface protocols, including serial interfaces (e.g., universal serial bus (USB) interfaces or IEEE interface standards), parallel interfaces, display adapters, audio adaptors, or custom interfaces. In some implementations, the user interface  410  is configured to communicate with one or more input/output devices, such as some combination of buttons, keyboards, pointing devices, displays, speakers, microphones, touch screens, and other devices. 
     The memory  420  includes volatile memory devices (e.g., random access memory (RAM) devices), nonvolatile memory devices (e.g., read-only memory (ROM) devices, programmable read-only memory, and flash memory), or both. The memory  420  is configured to store instructions  422 . The processor  404  is configured to execute the instructions  422  to perform the operations described herein. To illustrate, the processor  404  may execute the instructions  422  to obtain a training data set  424  and use the training data set  424  to generate and train the out-of-domain sentence detector  426 , in a similar manner to as described with reference to  FIG. 1 . For example, in a particular implementation, the instructions  422  include classifier training instructions, additional text data generation instructions, autoencoder training instructions, forcing function instructions, and combining instructions. The out-of-domain sentence detector  426  may be stored at the memory  420  for execution by the computing device  402 . In some implementations, a pool of example sentences is stored at the memory  420 , and example sentences from the pool may be used as additional text data in training the out-of-domain sentence detector  426 . Alternatively, the pool may be external to the computing device  402  and accessible to the computing device  402 , such as via the transmitter  406  or the receiver  408 . Additionally, or alternatively, the transmitter  406  may be configured to transmit the out-of-domain sentence detector  426  to a different device for execution at the different device. 
       FIG. 5  is a flowchart of a method  500  for training an out-of-domain sentence detector. In an illustrative example, the method  500  is performed by the system  100  of  FIG. 1  or the computing device  402  of  FIG. 4 . 
     The method  500  includes obtaining a training data set including text data indicating one or more phrases or sentences, at  502 . For example, the system  100  obtains the training data set  110 , such as from a customer. 
     The method  500  includes training a classifier using supervised machine learning based on the training data set and additional text data indicating one or more out-of-domain phrases or sentences, at  504 . For example, the classifier  104  is trained using the training data set  110  and the additional text data  114  (e.g., text data indicating one or more out-of-domain phrases or sentences). In a particular implementation, the training data set and at least a portion of the additional text data are obtained from a customer. 
     The method  500  includes training an autoencoder using unsupervised machine learning based on the training data, at  506 . For example, the autoencoder  106  is trained using the training data set  110  (e.g., the in-domain training examples without the labels). In a particular implementation, the autoencoder is configured to output a reconstruction error, and the second output is based on application of a forcing function to the reconstruction error. For example, the autoencoder  106  generates the reconstruction error  118 , and the forcing function  120  is applied to the reconstruction error  118  to generate the second output  124 . 
     The method  500  further includes combining the classifier and the autoencoder to generate an out-of-domain sentence detector configured to generate an output indicating a classification of whether input text data corresponds to an out-of-domain sentence, at  508 . The output is based on a combination of a first output of the classifier and a second output of the autoencoder. For example, the out-of-domain sentence detector  102  includes the combiner  108  that is configured to generate the classification  126  based on a combination of the first output  122  and the second output  124 . The classification  126  indicates whether the input text data  116  corresponds to an out-of-domain sentence (or an in-domain sentence). In a particular implementation, the output includes an average of the first output and a value based on the second output. In another particular implementation, the output includes a weighted average of the first output and a value based on the second output. 
     In a particular implementation, at least a portion of the additional text data is obtained from a pool of example sentences. For example, at least a portion of the additional text data  114  may be obtained from the example sentence pool  112 . In this implementation, the method  500  may further include clustering the example sentences of the pool into clusters in a feature space, where a distance between clusters in the feature space indicates a similarity between sentence examples in the clusters. For example, the example sentences in the example sentence pool  112  may be clustered into clusters in a feature space, as described with reference to  FIGS. 2A and 2B . In some implementations, the method  500  also includes obtaining an out-of-domain example sentence, mapping the out-of-domain example sentence into the feature space, and including a particular example sentence from the pool in the portion of the additional text data based on a distance in the feature space between the out-of-domain example sentence and the particular example sentence failing to satisfy a first threshold. For example, as described with reference to  FIG. 2A , the particular sentence  204  is added to the additional text data  210  (e.g., a group of one or more out-of-domain example sentences) based on the distance in the feature space between the particular sentence  204  and the out-of-domain example sentence  202  (e.g., an out-of-domain sentence provided by a customer) failing to satisfy the first threshold  206 . The distance may include a cosine distance or an L 2  distance, as non-limiting examples. In some implementations, the method  500  also includes mapping an example sentence from the training data set into the feature space and including a particular example sentence from the pool in the portion of the additional text data based on a distance in the feature space between the example sentence and the particular example sentence satisfying a second threshold. For example, as described with reference to  FIG. 2B , the particular sentence  224  is added to the additional text data  210  (e.g., a group of one or more out-of-domain example sentences) based on the distance in the feature space between the particular sentence  224  and the in-domain example sentence  222  (e.g., an in-domain sentence provided by a customer) satisfying the second threshold  208 . 
     In some implementations, the training data set includes a first number of training examples, the additional text data includes a second number of training examples, and the first number and the second number are the same. For example, the number of training examples in the additional text data  210  (or the additional text data  114  of  FIG. 1 ) that are received from the customer, obtained from the example sentence pool  112 , or a combination thereof, is equal to the number of training examples in the training data set  110 . Having a substantially equal number of in-domain training examples and out-of-domain training examples may improve training the classifier. 
     In a particular implementation, providing the training data set to the autoencoder includes generating one or more embedding vectors based on the training data set and providing the one or more embedding vectors to the autoencoder. For example, as described with reference to  FIG. 3 , the embedding vectors  314  may be generated based on the word representations  306  and the in-domain sentences  308 . In this implementation, the one or more embedding vectors may be generated using a long short-term memory (LSTM) network, a universal sentence encoder (USE), or embeddings for language models (ELMo), as non-limiting examples. 
     One benefit provided by method  500  is the generation and training of an out-of-domain sentence detector that is more robust and more accurate than other out-of-domain sentence detectors. By accurately detecting out-of-domain sentences, proper actions may be taken, such as requesting a user to resubmit a request that is more on-topic, instead of providing a response that is possibly outside of what the user would expect to a given request. 
       FIG. 6  is a flowchart that illustrates an example of a method of deploying an out-of-domain sentence detector according to an implementation of the present invention. While it is understood that process software, such as the out-of-domain sentence detector  102  of  FIG. 1  or the out-of-domain sentence detector  426  of  FIG. 4 , may be deployed by manually loading it directly in the client, server, and proxy computers via loading a storage medium such as a CD, DVD, etc., the process software may also be automatically or semi-automatically deployed into a computer system by sending the process software to a central server or a group of central servers. The process software is then downloaded into the client computers that will execute the process software. Alternatively, the process software is sent directly to the client system via e-mail. The process software is then either detached to a directory or loaded into a directory by executing a set of program instructions that detaches the process software into a directory. Another alternative is to send the process software directly to a directory on the client computer hard drive. When there are proxy servers, the process will select the proxy server code, determine on which computers to place the proxy servers&#39; code, transmit the proxy server code, and then install the proxy server code on the proxy computer. The process software will be transmitted to the proxy server, and then it will be stored on the proxy server. 
     Step  600  begins the deployment of the process software. An initial step is to determine if there are any programs that will reside on a server or servers when the process software is executed ( 601 ). If this is the case, then the servers that will contain the executables are identified ( 619 ). The process software for the server or servers is transferred directly to the servers&#39; storage via FTP or some other protocol or by copying though the use of a shared file system ( 620 ). The process software is then installed on the servers ( 621 ). 
     Next, a determination is made on whether the process software is to be deployed by having users access the process software on a server or servers ( 602 ). If the users are to access the process software on servers, then the server addresses that will store the process software are identified ( 603 ). 
     A determination is made if a proxy server is to be built ( 609 ) to store the process software. A proxy server is a server that sits between a client application, such as a Web browser, and a real server. It intercepts all requests to the real server to see if it can fulfill the requests itself. If not, it forwards the request to the real server. The two primary benefits of a proxy server are to improve performance and to filter requests. If a proxy server is required, then the proxy server is installed ( 610 ). The process software is sent to the (one or more) servers either via a protocol such as FTP, or it is copied directly from the source files to the server files via file sharing ( 611 ). Another embodiment involves sending a transaction to the (one or more) servers that contained the process software, and have the server process the transaction and then receive and copy the process software to the server&#39;s file system. Once the process software is stored at the servers, the users via their client computers then access the process software on the servers and copy to their client computers file systems ( 612 ). Another embodiment is to have the servers automatically copy the process software to each client and then run the installation program for the process software at each client computer. The user executes the program that installs the process software on his client computer ( 618 ) and then exits the process ( 608 ). 
     In step  604  a determination is made whether the process software is to be deployed by sending the process software to users via e-mail. The set of users where the process software will be deployed are identified together with the addresses of the user client computers ( 605 ). The process software is sent via e-mail to each of the users&#39; client computers ( 613 ). The users then receive the e-mail ( 614 ) and then detach the process software from the e-mail to a directory on their client computers ( 615 ). The user executes the program that installs the process software on his client computer ( 618 ) and then exits the process ( 608 ). 
     Lastly, a determination is made on whether the process software will be sent directly to user directories on their client computers ( 606 ). If so, the user directories are identified ( 607 ). The process software is transferred directly to the user&#39;s client computer directory ( 616 ). This can be done in several ways such as, but not limited to, sharing the file system directories and then copying from the sender&#39;s file system to the recipient user&#39;s file system or, alternatively, using a transfer protocol such as File Transfer Protocol (FTP). The users access the directories on their client file systems in preparation for installing the process software ( 617 ). The user executes the program that installs the process software on his client computer ( 618 ) and then exits the process ( 608 ). 
       FIG. 7  is a flowchart that illustrates an example of a method of using an out-of-domain sentence detector in an on demand context. In  FIG. 7 , the process software, such as the out-of-domain sentence detector  102  of  FIG. 1  or the out-of-domain sentence detector  426  of  FIG. 4 , may also be shared, simultaneously serving multiple customers in a flexible, automated fashion. It is standardized, requiring little customization, and it is scalable, providing capacity on demand in a pay-as-you-go model. 
     The process software can be stored on a shared file system accessible from one or more servers. The process software is executed via transactions that contain data and server processing requests that use CPU units on the accessed server. CPU units are units of time, such as minutes, seconds, and hours, on the central processor of the server. Additionally, the accessed server may make requests of other servers that require CPU units. CPU units are an example that represents but one measurement of use. Other measurements of use include, but are not limited to, network bandwidth, memory usage, storage usage, packet transfers, complete transactions, etc. 
     When multiple customers use the same process software application, their transactions are differentiated by the parameters included in the transactions that identify the unique customer and the type of service for that customer. All of the CPU units and other measurements of use that are used for the services for each customer are recorded. When the number of transactions to any one server reaches a number that begins to affect the performance of that server, other servers are accessed to increase the capacity and to share the workload. Likewise, when other measurements of use, such as network bandwidth, memory usage, storage usage, etc., approach a capacity so as to affect performance, additional network bandwidth, memory usage, storage, etc. are added to share the workload. 
     The measurements of use employed for each service and customer are sent to a collecting server that sums the measurements of use for each customer for each service that was processed anywhere in the network of servers that provide the shared execution of the process software. The summed measurements of use units are periodically multiplied by unit costs, and the resulting total process software application service costs are alternatively sent to the customer and/or indicated on a web site accessed by the customer, who may then remit payment to the service provider. 
     In another embodiment, the service provider requests payment directly from a customer account at a banking or financial institution. 
     In another embodiment, if the service provider is also a customer of the customer that uses the process software application, the payment owed to the service provider is reconciled to the payment owed by the service provider to minimize the transfer of payments. 
     Step  700  begins the On Demand process. A transaction is created that contains the unique customer identification, the requested service type, and any service parameters that further specify the type of service ( 702 ). The transaction is then sent to the main server ( 704 ). In an On Demand environment, the main server can initially be the only server, and then as capacity is consumed other servers are added to the On Demand environment. 
     The server central processing unit (CPU) capacities in the On Demand environment are queried ( 706 ). The CPU requirement of the transaction is estimated, and then the server&#39;s available CPU capacity in the On Demand environment is compared to the transaction CPU requirement to see if there is sufficient CPU available capacity in any server to process the transaction ( 708 ). If there is not sufficient server CPU available capacity, then additional server CPU capacity is allocated to process the transaction ( 710 ). If there was already sufficient available CPU capacity, then the transaction is sent to a selected server ( 712 ). 
     Before executing the transaction, a check is made of the remaining On Demand environment to determine if the environment has sufficient available capacity for processing the transaction. This environment capacity consists of such things as, but not limited to, network bandwidth, processor memory, storage etc. ( 714 ). If there is not sufficient available capacity, then capacity will be added to the On Demand environment ( 716 ). Next the required software to process the transaction is accessed, loaded into memory, and then the transaction is executed ( 718 ). 
     The usage measurements are recorded ( 720 ). The usage measurements consist of the portions of those functions in the On Demand environment that are used to process the transaction. The usage of such functions as, but not limited to, network bandwidth, processor memory, storage and CPU cycles are what is recorded. The usage measurements are summed, multiplied by unit costs, and then recorded as a charge to the requesting customer ( 722 ). 
     If the customer has requested that the On Demand costs be posted to a web site ( 724 ), then they are posted thereto ( 726 ). If the customer has requested that the On Demand costs be sent via e-mail to a customer address ( 728 ), then they are sent ( 730 ). If the customer has requested that the On Demand costs be paid directly from a customer account ( 732 ), then payment is received directly from the customer account ( 734 ). On Demand process proceeds to  736  and exits. 
     It is to be understood that although this disclosure includes a detailed description on cloud computing, implementation of the teachings recited herein are not limited to a cloud computing environment. Rather, embodiments of the present invention are capable of being implemented in conjunction with any other type of computing environment now known or later developed. 
     Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. This cloud model may include at least five characteristics, at least three service models, and at least four deployment models. 
     Characteristics are as follows: 
     On-demand self-service: a cloud consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with the service&#39;s provider. 
     Broad network access: capabilities are available over a network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs). 
     Resource pooling: the provider&#39;s computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand. There is a sense of location independence in that the consumer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter). 
     Rapid elasticity: capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time. 
     Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency for both the provider and consumer of the utilized service. 
     Service Models are as follows: 
     Software as a Service (SaaS): the capability provided to the consumer is to use the provider&#39;s applications running on a cloud infrastructure. The applications are accessible from various client devices through a thin client interface such as a web browser (e.g., web-based e-mail). The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings. 
     Platform as a Service (PaaS): the capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages and tools supported by the provider. The consumer does not manage or control the underlying cloud infrastructure including networks, servers, operating systems, or storage, but has control over the deployed applications and possibly application hosting environment configurations. 
     Infrastructure as a Service (IaaS): the capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls). 
     Deployment Models are as follows: 
     Private cloud: the cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may exist on-premises or off-premises. 
     Community cloud: the cloud infrastructure is shared by several organizations and supports a specific community that has shared concerns (e.g., mission, security requirements, policy, and compliance considerations). It may be managed by the organizations or a third party and may exist on-premises or off-premises. 
     Public cloud: the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services. 
     Hybrid cloud: the cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load-balancing between clouds). 
     A cloud computing environment is service oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure that includes a network of interconnected nodes. 
     Referring to  FIG. 8 , illustrative cloud computing environment  50  is depicted. As shown, cloud computing environment  50  includes one or more cloud computing nodes  10  with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone  54 A, desktop computer  54 B, laptop computer  54 C, and/or automobile computer system  54 N may communicate. Nodes  10  may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described hereinabove, or a combination thereof. This allows cloud computing environment  50  to offer infrastructure, platforms and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices  54 A-N shown in  FIG. 8  are intended to be illustrative only and that computing nodes  10  and cloud computing environment  50  can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser). In a particular implementation, one or more of the nodes  10  include the out-of-domain sentence detector  102  of  FIG. 1 . 
     Referring to  FIG. 9 , a set of functional abstraction layers provided by cloud computing environment  50  ( FIG. 8 ) is shown. It should be understood in advance that the components, layers, and functions shown in  FIG. 9  are intended to be illustrative only and embodiments of the invention are not limited thereto. As depicted, the following layers and corresponding functions are provided: 
     Hardware and software layer  60  includes hardware and software components. Examples of hardware components include: mainframes  61 ; RISC (Reduced Instruction Set Computer) architecture-based servers  62 ; servers  63 ; blade servers  64 ; storage devices  65 ; and networks and networking components  66 . In some embodiments, software components include network application server software  67  and database software  68 . 
     Virtualization layer  70  provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers  71 ; virtual storage  72 ; virtual networks  73 , including virtual private networks; virtual applications and operating systems  74 ; and virtual clients  75 . 
     In one example, management layer  80  may provide the functions described below. Resource provisioning  81  provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing  82  provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one example, these resources may include application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. User portal  83  provides access to the cloud computing environment for consumers and system administrators. Service level management  84  provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment  85  provide pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA. 
     Workloads layer  90  provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation  91 ; software development and lifecycle management  92 ; virtual classroom education delivery  93 ; data analytics processing  94 ; transaction processing  95 ; and out-of-domain sentence detection  96 . For example, the out-of-domain sentence detection  96  may use or have access to an out-of-domain sentence detector, such as the out-of-domain sentence detector  102  of  FIG. 1  or the out-of-domain sentence detector  426  of  FIG. 4 . 
     The present invention may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention. 
     The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. 
     Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device. 
     Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++, or the like, and procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention. 
     Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions. 
     These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions. 
     The descriptions of the various embodiments of the present invention have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.