Abstract: A method for authoring a conversational bot including: receiving conversation data; receiving seed intent data that comprises seed intents having a seed intent label and sample intent-bearing utterances; using an intent mining algorithm to mine the conversation data to determine new utterances to associate with the seed intent; augmenting the seed intent data to include the mined new utterances associated with the seed intents; and uploading the augmented seed intent data into the conversation bot. The intent mining algorithm may include: identifying intent-bearing utterances; identifying candidate intents; for each of the seed intents, identifying seed intent alternatives from the sample intent-bearing utterances; associating the intent-bearing utterances from the conversation data with the seed intents via determining a degree of semantic similarity between the candidate intents of the intent-bearing utterances and the seed intent alternatives.

Abstract: A method for mining topics discussed in conversations that includes: receiving conversation data; and using a topic mining algorithm to mine topics from the conversation data. The topic mining algorithm includes identifying candidate topics in each of the conversations. The topic mining algorithm further includes identifying the topics of the conversations by: compiling a list of the candidate topics; pruning the list of candidate topics by discarding certain of the candidate topics per a cross-conversation factor that factors usage across all conversations; and identifying the candidate topics remaining on the pruned list of candidate topics as the topics. The topic mining algorithm further includes determining topic groups by grouping the topics according to a degree of semantic similarity between the topics; and associating a list of utterances with the topic groups.

Abstract: A method of leveraging a global confidence classifier for information retrieval in contact centers according to an embodiment includes receiving, by a computing system, a user query from a contact center client communication with a knowledge-only bot of the computing system, performing, by the computing system, feature extraction on the user query by converting query words of the user query into a numerical vector representation of the user query, identifying, by the computing system, a subset of documents most likely to be responsive to the user query, and re-ranking, by the computing system, the subset of documents most likely to be responsive to the user query based on a global confidence classifier model.

Abstract: A method for creating a student model from a teacher model for knowledge distillation. The method may include: providing the teacher model trained on a first training dataset; generating candidate student models, wherein each of the candidate student models is a model having a unique permutation of layers derived by randomly selecting one or more layers of the plurality of layers of the teacher model for removing; generating a second training dataset; for each of the candidate student models: providing the second training dataset as inputs; recording outputs generated; and based on the recorded outputs, evaluating a performance according to a predetermined model evaluation criterion; determining which of the candidate student models performed best among the candidate student models based on the predetermined model evaluation criterion; identifying a preferred candidate student model.

Abstract: A method for creating a student model from a teacher model for knowledge distillation. The method including: providing a first model; using a first instance of the first model to create the teacher model by training the first instance of the first model on a training dataset; using a second instance of the first model to create the student model by training the second instance of the first model on a subset of the training dataset; identifying corresponding layers in the teacher model and the student model; for each of the corresponding layers, computing a weight similarity criterion; ranking the corresponding layers according to the weight similarity criterion; selecting, based on the ranking, one or more of the corresponding layers for designation as one or more discard layers; removing from the student model the one or more discard layers.

Abstract: A method of applying a confidence classifier for intent classification in association with an automated chat bot according to an embodiment includes processing, by a computing system, an utterance with an intent classifier to determine a probability distribution of possible intents associated with the utterance, generating, by the computing system, a plurality of measures of peakedness of the probability distribution, and applying, by the computing system, a trained confidence classifier to determine a single normalized probability of a most likely intent associated with the utterance based on the plurality of measures of peakedness of the probability distribution.

Abstract: A method for intent mining that includes: receiving conversation data; using an intent mining algorithm to automatically mine intents from the conversation data; and uploading the mined intents into the conversational bot. The intent mining algorithm may include: analyzing utterances of the conversation data to identify intent-bearing utterances; analyzing the identified intent-bearing utterances to identify candidate intents; selecting salient intents from the candidate intents; grouping the selected salient intents into salient intent groups in accordance with a degree of semantic similarity; for each of the salient intent groups, selecting one of the salient intents as the intent label and designating the others as the intent alternatives; and associating the intent-bearing utterances with the salient intent groups via determining a degree of semantic similarity between the candidate intents present in the intent-bearing utterance and the intent alternatives within each group.

Abstract: A method of applying a confidence classifier for intent classification in association with an automated chat bot according to an embodiment includes processing, by a computing system, an utterance with an intent classifier to determine a probability distribution of possible intents associated with the utterance, generating, by the computing system, a plurality of measures of peakedness of the probability distribution, and applying, by the computing system, a trained confidence classifier to determine a single normalized probability of a most likely intent associated with the utterance based on the plurality of measures of peakedness of the probability distribution.

Abstract: A method for authoring a conversational bot including: receiving conversation data; receiving seed intent data that comprises seed intents having a seed intent label and sample intent-bearing utterances; using an intent mining algorithm to mine the conversation data to determine new utterances to associate with the seed intent; augmenting the seed intent data to include the mined new utterances associated with the seed intents; and uploading the augmented seed intent data into the conversation bot. The intent mining algorithm may include: identifying intent-bearing utterances; identifying candidate intents; for each of the seed intents, identifying seed intent alternatives from the sample intent-bearing utterances; associating the intent-bearing utterances from the conversation data with the seed intents via determining a degree of semantic similarity between the candidate intents of the intent-bearing utterances and the seed intent alternatives.

Abstract: A method for intent mining that includes: receiving conversation data; using an intent mining algorithm to automatically mine intents from the conversation data; and uploading the mined intents into the conversational bot. The intent mining algorithm may include: analyzing utterances of the conversation data to identify intent-bearing utterances; analyzing the identified intent-bearing utterances to identify candidate intents; selecting salient intents from the candidate intents; grouping the selected salient intents into salient intent groups in accordance with a degree of semantic similarity; for each of the salient intent groups, selecting one of the salient intents as the intent label and designating the others as the intent alternatives; and associating the intent-bearing utterances with the salient intent groups via determining a degree of semantic similarity between the candidate intents present in the intent-bearing utterance and the intent alternatives within each group.

Abstract: A system and method are presented for a multiclass approach for confidence modeling in automatic speech recognition systems. A confidence model may be trained offline using supervised learning. A decoding module is utilized within the system that generates features for audio files in audio data. The features are used to generate a hypothesized segment of speech which is compared to a known segment of speech using edit distances. Comparisons are labeled from one of a plurality of output classes. The labels correspond to the degree to which speech is converted to text correctly or not. The trained confidence models can be applied in a variety of systems, including interactive voice response systems, keyword spotters, and open-ended dialog systems.

Abstract: A method for automated generation of contact center system embeddings according to one embodiment includes determining, by a computing system, contact center system agents, contact center system agent skills, and/or contact center system virtual queue experiences; generating, by the computing system, a matrix representation based on the contact center system agents, the contact center system agent skills, and/or the contact center system virtual queue experiences; generating, by the computing system and based on the matrix representation, contact center system agent identifiers, contact center system agent skills identifiers, and/or contact center system virtual queue identifiers; transforming, by the computing system, the contact center system agent identifiers, the contact center system agent skills identifiers, and/or the contact center system virtual queue identifiers into the contact center system agent embeddings, contact center system agent skills embeddings, and/or contact center system virtual queue

Abstract: A system and method are presented for a multiclass approach for confidence modeling in automatic speech recognition systems. A confidence model may be trained offline using supervised learning. A decoding module is utilized within the system that generates features for audio files in audio data. The features are used to generate a hypothesized segment of speech which is compared to a known segment of speech using edit distances. Comparisons are labeled from one of a plurality of output classes. The labels correspond to the degree to which speech is converted to text correctly or not. The trained confidence models can be applied in a variety of systems, including interactive voice response systems, keyword spotters, and open-ended dialog systems.