  As conversational search becomes more pervasive, it becomes increasingly
important to understand the user's underlying information needs when they
converse with such systems in diverse domains. We conduct an in-situ study to
understand information needs arising in a home cooking context as well as how
they are verbally communicated to an assistant. A human experimenter plays this
role in our study. Based on the transcriptions of utterances, we derive a
detailed hierarchical taxonomy of diverse information needs occurring in this
context, which require different levels of assistance to be solved. The
taxonomy shows that needs can be communicated through different linguistic
means and require different amounts of context to be understood. In a second
contribution we perform classification experiments to determine the feasibility
of predicting the type of information need a user has during a dialogue using
the turn provided. For this multi-label classification problem, we achieve
average F1 measures of 40% using BERT-based models. We demonstrate with
examples, which types of need are difficult to predict and show why, concluding
that models need to include more context information in order to improve both
information need classification and assistance to make such systems usable.
