Could somebody explain why positional embedding is added to the token embedding?

Hello,

I'm reading this tutorial on positional embedding of a transformer architecture. [https://machinelearningmastery.com/a-gentle-introduction-to-positional-encoding-in-transformer-models-part-1/](https://machinelearningmastery.com/a-gentle-introduction-to-positional-encoding-in-transformer-models-part-1/)

I don't understand the very last part of it:

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>What Is the Final Output of the Positional Encoding Layer?
>
>The positional encoding layer sums the positional vector with the word encoding and outputs this matrix for the subsequent layers. The entire process is shown below.

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Basically it says the token (word) embedding should be added with the positional embedding. What's the justification for that? …

deeplearning embedding encoding hello layer matrix part positional encoding process token vector word