Retrieval-based, Self-supervised Augmentation Using Transformer Models

Abstract

Embodiments of the invention are directed to retrieval-based, self-supervised augmentation using transformer models. The system accesses query information associated with a to-be-augmented information set, generates sequence vectors representing the query information and the information set, identifies matching unannotated data repository vectors, and generates a response based at least in part on the matches.