Abstract: An intelligent computer platform to introduce adversarial training to natural language processing (NLP). An initial training set is modified with synthetic training data to create an adversarial training set. The modification includes use of natural language understanding (NLU) to parse the initial training set into components and identify component categories. As input is presented, a classifier evaluates the input and leverages the adversarial training set to identify the intent of the input. An identified classification model generates accurate and reflective response data based on the received input.

Abstract: An intelligent computer platform to introduce adversarial training to natural language processing (NLP). An initial training set is modified with synthetic training data to create an adversarial training set. The modification includes use of natural language understanding (NLU) to parse the initial training set into components and identify component categories. One or more paraphrase terms are identified with respect to the components and component categories, and function as replacement terms. The synthetic training data is effectively a merging of the initial training set with the replacement terms. As input is presented, a classifier leverages the adversarial training set to identify the intent of the input and to output a classification label to generate accurate and reflective response data.

Abstract: A computer-implemented method 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.

Abstract: A computer-implemented method 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.

Abstract: An intelligent computer platform to introduce adversarial training to natural language processing (NLP). An initial training set is modified with synthetic training data to create an adversarial training set. The modification includes use of natural language understanding (NLU) to parse the initial training set into components and identify component categories. One or more paraphrase terms are identified with respect to the components and component categories, and function as replacement terms. The synthetic training data is effectively a merging of the initial training set with the replacement terms. As input is presented, a classifier leverages the adversarial training set to identify the intent of the input and to output a classification label to generate accurate and reflective response data.

Abstract: An intelligent computer platform to introduce adversarial training to natural language processing (NLP). An initial training set is modified with synthetic training data to create an adversarial training set. The modification includes use of natural language understanding (NLU) to parse the initial training set into components and identify component categories. As input is presented, a classifier evaluates the input and leverages the adversarial training set to identify the intent of the input. An identified classification model generates accurate and reflective response data based on the received input.