We address unsupervised discontinuous constituency parsing, where we observe
a high variance in the performance of the only previous model. We propose to
build an ensemble of different runs of the existing discontinuous parser by
averaging the predicted trees, to stabilize and boost performance. To begin
with, we provide comprehensive computational complexity analysis (in terms of P
and NP-complete) for tree averaging under different setups of binarity and
continuity. We then develop an efficient exact algorithm to tackle the task,
which runs in a reasonable time for all samples in our experiments. Results on
three datasets show our method outperforms all baselines in all metrics; we
also provide in-depth analyses of our approach.

我们提出了一种新的集成方法来处理无监督的不连续句法分析问题，通过对现有的不连续解析器的多次运行进行平均预测，稳定并提升性能。同时，我们还提供了对于不同二进制和连续性设置下的树平均的综合计算复杂性分析，以及一种高效的精确算法来解决这个任务，实验结果表明我们的方法在三个数据集上优于所有基准，并对我们的方法进行了深入分析。

基于集成的无监督非连续性短语结构分析方法

Ensemble-Based Unsupervised Discontinuous Constituency Parsing by Tree  Averaging

We introduce a novel transition system for discontinuous constituency
parsing. Instead of storing subtrees in a stack --i.e. a data structure with
linear-time sequential access-- the proposed system uses a set of parsing
items, with constant-time random access. This change makes it possible to
construct any discontinuous constituency tree in exactly $4n - 2$ transitions
for a sentence of length $n$. At each parsing step, the parser considers every
item in the set to be combined with a focus item and to construct a new
constituent in a bottom-up fashion. The parsing strategy is based on the
assumption that most syntactic structures can be parsed incrementally and that
the set --the memory of the parser-- remains reasonably small on average.
Moreover, we introduce a provably correct dynamic oracle for the new transition
system, and present the first experiments in discontinuous constituency parsing
using a dynamic oracle. Our parser obtains state-of-the-art results on three
English and German discontinuous treebanks.

通过使用一种新的过渡系统，将存储子树的方法改为带有常数时间随机访问的解析项集合，设计了一种能够在 4n-2 次转换中完成长度为 n 的任何不连续成分树构建的解析策略，同时引入了一个可靠的动态预测器用于该过渡系统，并在三个英语和德语的不连续成分分析树库上实现了最先进的结果。

基于无栈转移系统和动态 Oracle 的不连续短语结构分析

Discontinuous Constituency Parsing with a Stack-Free Transition System  and a Dynamic Oracle

Lexicalized parsing models are based on the assumptions that (i) constituents
are organized around a lexical head (ii) bilexical statistics are crucial to
solve ambiguities. In this paper, we introduce an unlexicalized
transition-based parser for discontinuous constituency structures, based on a
structure-label transition system and a bi-LSTM scoring system. We compare it
to lexicalized parsing models in order to address the question of
lexicalization in the context of discontinuous constituency parsing. Our
experiments show that unlexicalized models systematically achieve higher
results than lexicalized models, and provide additional empirical evidence that
lexicalization is not necessary to achieve strong parsing results. Our best
unlexicalized model sets a new state of the art on English and German
discontinuous constituency treebanks. We further provide a per-phenomenon
analysis of its errors on discontinuous constituents.

本研究比较基于词汇化和非词汇化的句法分析模型的性能表现，提出了一种基于结构 - 标签转换系统和双向 LSTM 评分系统的非词汇化转换型分析器，结果表明非词汇化模型在处理不连续组分句法结构时具有更好的性能表现，尤其是在英语和德语的领域中。