Asset quality management using multimodality

Abstract

Disclosed herein are system, method, and computer program product embodiments that evaluate quality of metadata elements for use in a content selection graphical user interface. The metadata elements are processed using a deep neural network (DNN) trained to generate quality labels or scores for the metadata elements. Training metadata elements are processed using a large language model to generate asset embeddings corresponding to the training metadata elements. Similarity scores between pairs of the asset embeddings are computed. Training metadata elements having asset embeddings outside of first and second score thresholds are labeled with a first label indicative of 'bad' metadata, and training metadata elements having asset embeddings between the thresholds are labeled with a second label indicative of “good” metadata. The DNN is trained to perform the generation of the quality labels or scores using supervised learning, based on the labeled training metadata elements.