  We present MeeQA, a dataset for natural-language question answering over
meeting transcripts. It includes real questions asked during meetings by its
participants. The dataset contains 48K question-answer pairs, extracted from
422 meeting transcripts, spanning multiple domains. Questions in transcripts
pose a special challenge as they are not always clear, and considerable context
may be required in order to provide an answer. Further, many questions asked
during meetings are left unanswered. To improve baseline model performance on
this type of questions, we also propose a novel loss function, \emph{Flat
Hierarchical Loss}, designed to enhance performance over questions with no
answer in the text. Our experiments demonstrate the advantage of using our
approach over standard QA models.
