This allows us to model the dense query embedding differently from the dense embedding in the phrase index while sharing all parameters of the BERT encoder.
given the contextualized word representations of the question, we obtain the the dense query vector by where h 1 1 is the encoding corresponding to the (first) special token and we obtain the others in a similar way.