  In this paper, we address the problem of answering complex information needs
by conversing conversations with search engines, in the sense that users can
express their queries in natural language, and directly receivethe information
they need from a short system response in a conversational manner. Recently,
there have been some attempts towards a similar goal, e.g., studies on
Conversational Agents (CAs) and Conversational Search (CS). However, they
either do not address complex information needs, or they are limited to the
development of conceptual frameworks and/or laboratory-based user studies.
  We pursue two goals in this paper: (1) the creation of a suitable dataset,
the Search as a Conversation (SaaC) dataset, for the development of pipelines
for conversations with search engines, and (2) the development of
astate-of-the-art pipeline for conversations with search engines, the
Conversations with Search Engines (CaSE), using this dataset. SaaC is built
based on a multi-turn conversational search dataset, where we further employ
workers from a crowdsourcing platform to summarize each relevant passage into a
short, conversational response. CaSE enhances the state-of-the-art by
introducing a supporting token identification module and aprior-aware pointer
generator, which enables us to generate more accurate responses.
  We carry out experiments to show that CaSE is able to outperform strong
baselines. We also conduct extensive analyses on the SaaC dataset to show where
there is room for further improvement beyond CaSE. Finally, we release the SaaC
dataset and the code for CaSE and all models used for comparison to facilitate
future research on this topic.
