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.

本篇论文研究了 VP 协调结构中的复杂句法现象 - 动词省略，提出了一个基于文本直接操作和使用分割和重述范例以恢复缺失元素的联合解析任务。通过提出一个语用省略框架和自动可扩展数据集收集方法，得到了一个包含超过 10K 个自然出现的动词省略例子的大型数据集，并对此任务进行了各种神经基线的训练。最后，本文将我们的数据集、度量和模型作为该领域未来研究的起点。