  We compiled a new sentence splitting corpus that is composed of 203K pairs of
aligned complex source and simplified target sentences. Contrary to previously
proposed text simplification corpora, which contain only a small number of
split examples, we present a dataset where each input sentence is broken down
into a set of minimal propositions, i.e. a sequence of sound, self-contained
utterances with each of them presenting a minimal semantic unit that cannot be
further decomposed into meaningful propositions. This corpus is useful for
developing sentence splitting approaches that learn how to transform sentences
with a complex linguistic structure into a fine-grained representation of short
sentences that present a simple and more regular structure which is easier to
process for downstream applications and thus facilitates and improves their
performance.
