Systems and methods for automatically configuring training data for training machine learning models of a machine learning-based dialogue system including seeding training samples or curating a corpus of training data based on instances of training data identifiedas anomalous

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.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS[0001]This application claims the benefit of U.S. Provisional Application No. 62 / 779,236, filed 13 Dec. 2018, and U.S. Provisional Application No. 62 / 829,289, filed 4 Apr. 2019, which are incorporated in their entirety by this reference.GOVERNMENT RIGHTS[0002]The subject matter of the invention may be subject to U.S. Government Rights under National Science Foundation grants: NSF SBIR Phase 1 Grant—1622049 and NSF SBIR Phase 2 Grant—1738441.TECHNICAL FIELD[0003]The inventions herein relate generally to the machine learning and artificially intelligent dialogue systems fields, and more specifically to a new and useful system and method for intelligently training machine learning models of a machine learning-based conversational service in the machine learning field.BACKGROUND[0004]Modern virtual assistants and / or online chatbots may typically be employed to perform various tasks or services based on an interaction with a user. Typically, a user ...

AI Technical Summary

Benefits of technology

This patent relates to a method for curating a large amount of raw machine learning training data. The method involves identifying and discarding any invalid anomalous instances that may be present in the training data. This results in a smaller, more targeted dataset for further analysis and training. The technical effect of this method is improved efficiency and accuracy in machine learning training.

Problems solved by technology

While the rules-based approach for implementing a virtual assistant may be useful for addressing pointed or specific queries or commands made by a user, the rigid or finite nature of this approach severely limits a capability of a virtual assistant to address queries or commands from a user that exceed the scope of the finite realm of pointed and / or specific queries or commands that are addressable by the finite set of rules that drive the response operations of the virtual assistant.

That is, the modern virtual assistants implemented via a rules-based approach for generating responses to users may not fully satisfy queries and commands posed by a user for which there are no predetermined rules to provide a meaningful response or result to the user.

Additionally, while machine learning enhances capabilities of artificially intelligent conversational systems, inefficiencies continue to persist in training the underlying machine learning models performing classification and predictive functions of the artificially intelligent conversation systems.

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