Ensemble-Based Unsupervised Discontinuous Constituency Parsing by Tree
  Averaging
Behzad Shayegh Yuqiao Wen Lili Mou
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.