Abstract: A system and method for accelerated content classification and routing of digital files in a data handling and data governance service includes identifying a digital computer file; sequentially routing the digital computer file to one or more machine learning-based content classification models of a plurality of distinct machine learning-based content classification models based on a service-defined model instantiation and execution sequence, wherein: the service-defined model instantiation and execution sequence defines a model instantiation and execution order for the plurality of distinct machine learning-based content classification models that enables a fast content classification of the digital computer file while minimizing a computation time or runtime of the one or more machine learning-based content classification models; computing, via a machine learning-based filename classification model, a content classification inference based on extracted filename feature data of the digital computer file; and

Abstract: A system and method of curating machine learning training data for improving a predictive accuracy of a machine learning model includes sourcing training data samples based on seeding instructions; returning a corpus of unlabeled training data samples based on a search of data repositories; assigning a distinct classification labels to each of the training data samples of the corpus; computing efficacy metrics for an in-scope corpus of labeled training data samples derived from a subset of training data samples of the corpus that have been assigned one of the plurality of distinct classification labels, wherein the efficacy metrics identify whether the in-scope corpus of labeled training data samples is suitable for training a target machine learning model; and routing the in-scope corpus of labeled training data samples based on the efficacy metrics.

Abstract: Systems and methods of computing classifications for and migrating digital content that includes accessing a digital content corpus within a source data storage system; in response to accessing the digital content corpus, for each distinct item of digital content of the plurality of distinct items of digital content: computing, via one or more digital content machine learning classification models, a content classification inference; identifying automated digital content handling tasks of a plurality of distinct digital content handling tasks based on the content classification inference; executing the automated content handling tasks identified for each distinct item of digital content, wherein executing the automated content handling tasks includes: designating a storage location within a target data storage system based on the in-migration content classification inference; and migrating a respective item of digital content from the source data storage system to the designated storage location within the ta

Abstract: A machine learning-informed method executing automated workflows for digital file handling includes computing, by file classification machine learning models, a machine learning classification inference for each of a plurality of distinct digital files of a corpus of digital files; curating a plurality of distinct sub-corpora of digital files based on the machine learning classification inference associated with each of the plurality of distinct digital files, wherein the at least one machine learning classification inference comprises a digital file type classification inference of a plurality of distinct digital file type classification inferences; and selectiyely executing an automated digital file handling workflow for at least one sub-corpus based on the digital file type classification inference, wherein the automated digital file handling workflow includes a sequence of computer-executable tasks that, when executed, operates to modify one or more of a residency, permissions, and file metadata associate

Abstract: Systems and methods of computing classifications for and migrating digital content that includes accessing a digital content corpus within a source data storage system; in response to accessing the digital content corpus, for each distinct item of digital content of the plurality of distinct items of digital content: computing, via one or more digital content machine learning classification models, a content classification inference; identifying automated digital content handling tasks of a plurality of distinct digital content handling tasks based on the content classification inference; executing the automated content handling tasks identified for each distinct item of digital content, wherein executing the automated content handling tasks includes: designating a storage location within a target data storage system based on the in-migration content classification inference; and migrating a respective item of digital content from the source data storage system to the designated storage location within the ta

Abstract: Systems and methods of computing classifications for and migrating digital content that includes accessing a digital content corpus within a source data storage system; in response to accessing the digital content corpus, for each distinct item of digital content of the plurality of distinct items of digital content: computing, via one or more digital content machine learning classification models, a content classification inference; identifying automated digital content handling tasks of a plurality of distinct digital content handling tasks based on the content classification inference; executing the automated content handling tasks identified for each distinct item of digital content, wherein executing the automated content handling tasks includes: designating a storage location within a target data storage system based on the in-migration content classification inference; and migrating a respective item of digital content from the source data storage system to the designated storage location within the ta

Abstract: A system and method of implementing an intuitive search interface for tactically searching one or more annotated utterance corpora in a machine learning-based dialogue system includes identifying an utterance corpus query for searching one or more annotated utterance corpora of a machine learning-based dialogue system; interpreting the utterance corpus query by translating the utterance corpus query into one or more search expressions recognizable to an utterance sample retrieval program searchably interfacing with the one or more annotated utterance corpora of the machine learning-based dialogue system; retrieving one or more annotated utterance samples from the one or more annotated utterance corpora based on the interpretation of the utterance corpus query; and returning the one or more annotated utterance samples to an intuitive utterance corpus search interface.

Abstract: A system and method of implementing an intuitive search interface for tactically searching one or more annotated utterance corpora in a machine learning-based dialogue system includes identifying an utterance corpus query for searching one or more annotated utterance corpora of a machine learning-based dialogue system; interpreting the utterance corpus query by translating the utterance corpus query into one or more search expressions recognizable to an utterance sample retrieval program searchably interfacing with the one or more annotated utterance corpora of the machine learning-based dialogue system; retrieving one or more annotated utterance samples from the one or more annotated utterance corpora based on the interpretation of the utterance corpus query; and returning the one or more annotated utterance samples to an intuitive utterance corpus search interface.

Abstract: Systems and methods for intelligently training a subject machine learning model includes identifying new observations comprising a plurality of distinct samples unseen by a target model during a prior training; creating an incremental training corpus based on randomly sampling a collection of training data samples that includes a plurality of new observations and a plurality of historical training data samples used in the prior training of the target model; implementing a first training mode that includes an incremental training of the target model using samples from the incremental training corpus as model training input; computing performance metrics of the target model based on the incremental training; evaluating the performance metrics of the target model against training mode thresholds; and selectively choosing based on the evaluation one of maintaining the first training mode and automatically switching to a second training mode that includes a full retraining of the target model.

Abstract: Systems and methods for automatically detecting annotation discrepancies in annotated training data samples and repairing the annotated training data samples for a machine learning-based automated dialogue system include evaluating a corpus of a plurality of distinct training data samples; identifying one or more of a slot span defect and a slot label defect of a target annotated slot span of a target training data sample of the corpus based on the evaluation; and automatically correcting one or more annotations of the target annotated slot span based on the identified one or more of the slot span defect and the slot label defect.

Abstract: Systems and methods for automatically detecting annotation discrepancies in annotated training data samples and repairing the annotated training data samples for a machine learning-based automated dialogue system include evaluating a corpus of a plurality of distinct training data samples; identifying one or more of a slot span defect and a slot label defect of a target annotated slot span of a target training data sample of the corpus based on the evaluation; and automatically correcting one or more annotations of the target annotated slot span based on the identified one or more of the slot span defect and the slot label defect.

Abstract: Systems and methods for constructing an artificially diverse corpus of training data includes evaluating a corpus of utterance-based training data samples, identifying a slot replacement candidate; deriving distinct skeleton utterances that include the slot replacement candidate, wherein deriving the distinct skeleton utterances includes replacing slots of each of the plurality of distinct utterance training samples with one of a special token and proper slot classification labels; selecting a subset of the distinct skeleton utterances; converting each of the distinct skeleton utterances of the subset back to distinct utterance training samples while still maintaining the special token at a position of the slot replacement candidate; altering a percentage of the distinct utterance training samples with a distinct randomly-generated slot token value at the position of the slot replacement candidate; and constructing the artificially diverse corpus of training samples based on a collection of the percentage of

Abstract: Systems and methods for automatically detecting annotation discrepancies in annotated training data samples and repairing the annotated training data samples for a machine learning-based automated dialogue system include evaluating a corpus of a plurality of distinct training data samples; identifying one or more of a slot span defect and a slot label defect of a target annotated slot span of a target training data sample of the corpus based on the evaluation; and automatically correcting one or more annotations of the target annotated slot span based on the identified one or more of the slot span defect and the slot label defect.

Abstract: Systems and methods for constructing an artificially diverse corpus of training data includes evaluating a corpus of utterance-based training data samples, identifying a slot replacement candidate; deriving distinct skeleton utterances that include the slot replacement candidate, wherein deriving the distinct skeleton utterances includes replacing slots of each of the plurality of distinct utterance training samples with one of a special token and proper slot classification labels; selecting a subset of the distinct skeleton utterances; converting each of the distinct skeleton utterances of the subset back to distinct utterance training samples while still maintaining the special token at a position of the slot replacement candidate; altering a percentage of the distinct utterance training samples with a distinct randomly-generated slot token value at the position of the slot replacement candidate; and constructing the artificially diverse corpus of training samples based on a collection of the percentage of

Abstract: A system and method for improving a machine learning-based dialogue system includes: sourcing a corpus of raw machine learning training data from sources of training data based on a plurality of seed training samples, wherein the corpus of raw machine learning training data comprises a plurality of distinct instances of training data; generating a vector representation for each distinct instance of training data; identifying statistical characteristics of the corpus of raw machine learning training data based on a mapping of the vector representation for each distinct instance of training data; identifying anomalous instances of the plurality of distinct instances of training data of the corpus of raw machine learning training data based on the identified statistical characteristics of the corpus; and curating the corpus of raw machine learning training data based on each of the instances of training data identified as anomalous instances.

Abstract: A system and method for improving a machine learning-based dialogue system includes: sourcing a corpus of raw machine learning training data from sources of training data based on a plurality of seed training samples, wherein the corpus of raw machine learning training data comprises a plurality of distinct instances of training data; generating a vector representation for each distinct instance of training data; identifying statistical characteristics of the corpus of raw machine learning training data based on a mapping of the vector representation for each distinct instance of training data; identifying anomalous instances of the plurality of distinct instances of training data of the corpus of raw machine learning training data based on the identified statistical characteristics of the corpus; and curating the corpus of raw machine learning training data based on each of the instances of training data identified as anomalous instances.

Abstract: A system and method for improving a machine learning-based dialogue system includes: sourcing a corpus of raw machine learning training data from sources of training data based on a plurality of seed training samples, wherein the corpus of raw machine learning training data comprises a plurality of distinct instances of training data; generating a vector representation for each distinct instance of training data; identifying statistical characteristics of the corpus of raw machine learning training data based on a mapping of the vector representation for each distinct instance of training data; identifying anomalous instances of the plurality of distinct instances of training data of the corpus of raw machine learning training data based on the identified statistical characteristics of the corpus; and curating the corpus of raw machine learning training data based on each of the instances of training data identified as anomalous instances.