  This paper describes a compact and effective model for low-latency passage
retrieval in conversational search based on learned dense representations.
Prior to our work, the state-of-the-art approach uses a multi-stage pipeline
comprising conversational query reformulation and information retrieval
modules. Despite its effectiveness, such a pipeline often includes multiple
neural models that require long inference times. In addition, independently
optimizing each module ignores dependencies among them. To address these
shortcomings, we propose to integrate conversational query reformulation
directly into a dense retrieval model. To aid in this goal, we create a dataset
with pseudo-relevance labels for conversational search to overcome the lack of
training data and to explore different training strategies. We demonstrate that
our model effectively rewrites conversational queries as dense representations
in conversational search and open-domain question answering datasets. Finally,
after observing that our model learns to adjust the $L_2$ norm of query token
embeddings, we leverage this property for hybrid retrieval and to support error
analysis.
