The aim is to refine and align semantics in transformer models through a novel attention mechanism.
Introduced a systematic joint learning approach for query, key, and value embeddings.
Utilized an implicit deep learning model hierarchy to enhance attention mechanisms.
Demonstrated improved alignment of semantics in embeddings.
Showed that the proposed model enhances the effectiveness of transformer attention.
Abstract
We introduce systematic joint learning of query, key and value embeddings for transformer attention via an implicit deep learning model hierarchy that refines and aligns semantics.