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hello there welcome back to the new video today weamp;#39;ll be talking about this paper from Facebook AI University of Washington and Princeton University which is titled as dense passage retrieval for open domain question answering so the central idea of the paper is to introduce a retrieval system that is based out of dense Vector representation when it comes to finding a relevant passage for answering any question so a typical question answering system follows a certain flow where you have a question queue and a context C where both of them are fed to a certain model M and considering we are talking about the extractive scenario the model is expected to Output an answer a which is a certain phrase that lies in this context but in this scenario what we have explicitly done is that we have focused our model to kind of understand this question and try to only answer it based on the context that we have provided which in some sense is a closed form of question answering but what about
