Improving Attention Mechanism with Query-Value Interaction

8 Oct 2020 Chuhan Wu Fangzhao Wu Tao Qi Yongfeng Huang

Attention mechanism has played critical roles in various state-of-the-art NLP models such as Transformer and BERT. It can be formulated as a ternary function that maps the input queries, keys and values into an output by using a summation of values weighted by the attention weights derived from the interactions between queries and keys... (read more)

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