  In human conversations, ellipsis and coreference are commonly occurring
linguistic phenomena. Although these phenomena are a mean of making
human-machine conversations more fluent and natural, only few dialogue corpora
contain explicit indications on which turns contain ellipses and/or
coreferences. In this paper we address the task of automatically detecting
ellipsis and coreferences in conversational question answering. We propose to
use a multi-label classifier based on DistilBERT. Multi-label classification
and active learning are employed to compensate the limited amount of labeled
data. We show that these methods greatly enhance the performance of the
classifier for detecting these phenomena on a manually labeled dataset.
