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。