  Human-assisting systems such as dialogue systems must take thoughtful,
appropriate actions not only for clear and unambiguous user requests, but also
for ambiguous user requests, even if the users themselves are not aware of
their potential requirements. To construct such a dialogue agent, we collected
a corpus and developed a model that classifies ambiguous user requests into
corresponding system actions. In order to collect a high-quality corpus, we
asked workers to input antecedent user requests whose pre-defined actions could
be regarded as thoughtful. Although multiple actions could be identified as
thoughtful for a single user request, annotating all combinations of user
requests and system actions is impractical. For this reason, we fully annotated
only the test data and left the annotation of the training data incomplete. In
order to train the classification model on such training data, we applied the
positive/unlabeled (PU) learning method, which assumes that only a part of the
data is labeled with positive examples. The experimental results show that the
PU learning method achieved better performance than the general
positive/negative (PN) learning method to classify thoughtful actions given an
ambiguous user request.
