  This paper presents a novel task using real user data obtained in
human-machine conversation. The task concerns with denotation extraction from
answer hints collected interactively in a dialogue. The task is motivated by
the need for large amounts of training data for question answering dialogue
system development, where the data is often expensive and hard to collect.
Being able to collect denotation interactively and directly from users, one
could improve, for example, natural understanding components on-line and ease
the collection of the training data. This paper also presents introductory
results of evaluation of several denotation extraction models including
attention-based neural network approaches.
