  Conversational search is an approach to information retrieval (IR), where
users engage in a dialogue with an agent in order to satisfy their information
needs. Previous conceptual work described properties and actions a good agent
should exhibit. Unlike them, we present a novel conceptual model defined in
terms of conversational goals, which enables us to reason about current
research practices in conversational search. Based on the literature, we elicit
how existing tasks and test collections from the fields of IR, natural language
processing (NLP) and dialogue systems (DS) fit into this model. We describe a
set of characteristics that an ideal conversational search dataset should have.
Lastly, we introduce MANtIS (the code and dataset are available at
https://guzpenha.github.io/MANtIS/), a large-scale dataset containing
multi-domain and grounded information seeking dialogues that fulfill all of our
dataset desiderata. We provide baseline results for the conversation response
ranking and user intent prediction tasks.
