Attention-based classifier machine learning models using dual-positional-mode segment embeddings

Abstract

Various embodiments of the present invention use segment embeddings generated based at least in part on both intra-segment positional indicators for metadata labels within hierarchically-structured segments of a segment-ordered hierarchically-structured input data object as well as cross-segment position indicators for the noted hierarchically-structured segments. By increasing the amount of positional data supplied to an attention-based classifier machine learning model compared to existing single-positional-model approaches, various embodiments of the present invention enable increasing the amount of data patterns discovered by an attention-based classifier machine learning model during each training epoch, which increases training speed of the attention-based classifier machine learning model given a constant target predictive accuracy by using two levels/modes of positionality associated with a segment-ordered hierarchically-structured input data object to generate segment embeddings provided to an attention-based classifier machine learning model during training of the noted attention-based classifier machine learning model.