  Current conversational passage retrieval systems cast conversational search
into ad-hoc search by using an intermediate query resolution step that places
the user's question in context of the conversation. While the proposed methods
have proven effective, they still assume the availability of large-scale
question resolution and conversational search datasets. To waive the dependency
on the availability of such data, we adapt a pre-trained token-level dense
retriever on ad-hoc search data to perform conversational search with no
additional fine-tuning. The proposed method allows to contextualize the user
question within the conversation history, but restrict the matching only
between question and potential answer. Our experiments demonstrate the
effectiveness of the proposed approach. We also perform an analysis that
provides insights of how contextualization works in the latent space, in
essence introducing a bias towards salient terms from the conversation.
