  Conversational search (CS) plays a vital role in information retrieval. The
current state of the art approaches the task using a multi-stage pipeline
comprising conversational query reformulation and information seeking modules.
Despite of its effectiveness, such a pipeline often comprises multiple neural
models and thus requires long inference times. In addition, independently
optimizing the effectiveness of each module does not consider the relation
between modules in the pipeline. Thus, in this paper, we propose a single-stage
design, which supports end-to-end training and low-latency inference. To aid in
this goal, we create a synthetic dataset for CS to overcome the lack of
training data and explore different training strategies using this dataset.
Experiments demonstrate that our model yields competitive retrieval
effectiveness against state-of-the-art multi-stage approaches but with lower
latency. Furthermore, we show that improved retrieval effectiveness benefits
the downstream task of conversational question answering.
