Walmart Apollo, LLC

Komal Arvind Dhuri

Deepa Mohan

Shankara Bhargava Subramanya

Simral Chaudhary

Jorge Adrian Sanchez Castro

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.

Automatically augmenting and labeling conversational data for training machine learning models

This application relates to systems and methods for proactively predicting user intents on personal agents. In some examples, a user intent prediction system can include a computing device configured to obtain user intent data identifying a desired action by a user on a network-enabled tool. The computing device is further configured to obtain contextual data characterizing a user's interaction with the network-enabled tool. The computing device can then determine at least one predicted future intent of the user on the network-enabled tool based on the user intent data and the contextual data and present the at least one predicted future intent to the user.

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San Jose, CA, United States of America

Komal Arvind Dhuri