Determining context and intent in omnichannel communications using machine learning based artificial intelligence (ai) techniques

A method for determining intent in a digital conversation, comprising:

• receiving, by a processor, data associated with a conversation with a user via a communication channel, the data for training at least one model to determine intent in the conversation with the user;
• selecting, by the processor, any unlabeled data from the data associated with the conversation from the user, the selected unlabeled data having probability of impacting the at least one model;
• providing, by the processor, a hierarchical multi-intent data labeling framework for labeling a representative subset of data from at least one of the data associated with the conversation with the user or the selected unlabeled data;
• training, by the processor, the at least one model based on the labeled representative subset of data using a plurality of parameter choices;
• retaining, by the processor, at least one candidate model set from the at least one trained models based on global metric performance;
• generating, by the processor, a production-ready model based on the retained at least one candidate model set;
• and
• deploying, by the processor, the production-ready model for intent determination in the conversation with the user.

A non-transitory computer-readable storage medium having an executable stored thereon, which when executed instructs a processor to perform the following:

• receiving data associated with a conversation with a user via a communication channel, the data for training at least one model to determine intent in the conversation with the user;
• selecting any unlabeled data from the data associated with the conversation from the user, the selected unlabeled data having probability of impacting the at least one model;
• providing a hierarchical multi-intent data labeling framework for labeling a representative subset of data from at least one of the data associated with the conversation with the user or the selected unlabeled data;
• training the at least one model based on the labeled representative subset of data using a plurality of parameter choices;
• retaining at least one candidate model set from the at least one trained models based on global metric performance;
• generating a production-ready model based on the retained at least one candidate model set;
• and
• deploying the production-ready model for intent determination in the conversation with the user.