  Verbal omissions are complex syntactic phenomena in VP coordination
structures. They occur when verbs and (some of) their arguments are omitted
from subsequent clauses after being explicitly stated in an initial clause.
Recovering these omitted elements is necessary for accurate interpretation of
the sentence, and while humans easily and intuitively fill in the missing
information, state-of-the-art models continue to struggle with this task.
Previous work is limited to small-scale datasets, synthetic data creation
methods, and to resolution methods in the dependency-graph level. In this work
we propose a conjunct resolution task that operates directly on the text and
makes use of a split-and-rephrase paradigm in order to recover the missing
elements in the coordination structure. To this end, we first formulate a
pragmatic framework of verbal omissions which describes the different types of
omissions, and develop an automatic scalable collection method. Based on this
method, we curate a large dataset, containing over 10K examples of
naturally-occurring verbal omissions with crowd-sourced annotations of the
resolved conjuncts. We train various neural baselines for this task, and show
that while our best method obtains decent performance, it leaves ample space
for improvement. We propose our dataset, metrics and models as a starting point
for future research on this topic.
