  Several recent works have suggested to represent semantic relations with
questions and answers, decomposing textual information into separate
interrogative natural language statements. In this paper, we consider three
QA-based semantic tasks - namely, QA-SRL, QANom and QADiscourse, each targeting
a certain type of predication - and propose to regard them as jointly providing
a comprehensive representation of textual information. To promote this goal, we
investigate how to best utilize the power of sequence-to-sequence (seq2seq)
pre-trained language models, within the unique setup of semi-structured
outputs, consisting of an unordered set of question-answer pairs. We examine
different input and output linearization strategies, and assess the effect of
multitask learning and of simple data augmentation techniques in the setting of
imbalanced training data. Consequently, we release the first unified QASem
parsing tool, practical for downstream applications who can benefit from an
explicit, QA-based account of information units in a text.
