  Using a single model across various tasks is beneficial for training and
applying deep neural sequence models. We address the problem of developing
generalist representations of text that can be used to perform a range of
different tasks rather than being specialised to a single application. We focus
on processing short questions and developing an embedding for these questions
that is useful on a diverse set of problems, such as question topic
classification, equivalent question recognition, and question answering. This
paper introduces QBERT, a generalist model for processing questions. With
QBERT, we demonstrate how we can train a multi-task network that performs all
question-related tasks and has achieved similar performance compared to its
corresponding single-task models.
