It is shown that disjunction of two switch-lists can blow up the
representation size exponentially. Since switch-lists can be negated without
any increase in size, this shows that conjunction of switch-lists also leads to
an exponential blow-up in general.

本文研究了两个开关列表的析取可能会导致表示大小呈指数级增长，而由于开关列表可以否定而不增加大小，因此证明了开关列表的合取在一般情况下也会导致指数级爆炸。

开关列表无法高效进行析取和合取操作

No Efficient Disjunction or Conjunction of Switch-Lists

In this paper, we address the representation of coordinate constructions in
Enhanced Universal Dependencies (UD), where relevant dependency links are
propagated from conjunction heads to other conjuncts. English treebanks for
enhanced UD have been created from gold basic dependencies using a heuristic
rule-based converter, which propagates only core arguments. With the aim of
determining which set of links should be propagated from a semantic
perspective, we create a large-scale dataset of manually edited syntax graphs.
We identify several systematic errors in the original data, and propose to also
propagate adjuncts. We observe high inter-annotator agreement for this semantic
annotation task. Using our new manually verified dataset, we perform the first
principled comparison of rule-based and (partially novel) machine-learning
based methods for conjunction propagation for English. We show that learning
propagation rules is more effective than hand-designing heuristic rules. When
using automatic parses, our neural graph-parser based edge predictor
outperforms the currently predominant pipelinesusing a basic-layer tree parser
plus converters.

本文探讨了 Enhanced Universal Dependencies 中坐标结构的表示，通过创造一个大规模的数据集，手动编辑语法图，确定了应该在语义角度传播哪些依赖链接，比较了基于规则和基于机器学习的方法，并提出了一种基于神经图解析器的边缘预测器，超越了目前主要的基于基本层树解析器加转换器的流水线。