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I guess there is an assumption that $A(q, K, V)$ represents similarity between q and the words in the sentence. Actually $A(q, K, V)$ is encoding the relationships from q to the words in the sentence. The information about which other words in particular are important to the word under consideration has been already encoded by $q \cdot k^{<i>}$. ...

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First need to understand what problems BERT can solve or what kind of inference/prediction it can achieve. BERT Neural Network - EXPLAINED! Encoder in Transformer itself can learn: Relations among words (what word is most probable in a context). For instance, what word will fit in the BLANK in the context I take [BLANK] of the opportunity. Relations ...

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BERT is a stack of deep bidirectional transformer encoders that read the input sequence and generate meaning representations called embeddings. It uses multi-head attention to decide the meaning.

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