Automatically augmenting and labeling conversational data for training machine learning models

Abstract

A method implemented via execution of computing instructions configured to run at one or more processors and stored at one or more non-transitory computer-readable media. The method can include generating training data for an intent classification machine learning model by: (a) determining, via a text-to-text machine learning model, one or more respective paraphrases for each sample phrase of training phrases; (b) generating, via a label generating machine learning model, labeled data based on unlabeled live logs by: (i) determining live-log samples from the unlabeled live logs based at least in part on: a respective timestamp of each live log of the unlabeled live logs, or random sampling; and (ii) generating, via the label generating machine learning model, the labeled data based on the live-log samples and one or more labeling functions; and (c) adding the one or more respective paraphrases for the each sample phrase of the training phrases and the labeled data to the training data. In certain embodiments, a respective quantity of the one or more respective paraphrases can vary for the each sample phrase of the training phrases. In some embodiments, the method further can include transmitting the training data, as generated, to the intent classification machine learning model for training. Other embodiments are described.