  Dependency parsing is an essential task in NLP, and the quality of dependency
parsers is crucial for many downstream tasks. Parsers' quality often varies
depending on the domain and the language involved. Therefore, it is essential
to combat the issue of varying quality to achieve stable performance. In
various NLP tasks, aggregation methods are used for post-processing aggregation
and have been shown to combat the issue of varying quality. However,
aggregation methods for post-processing aggregation have not been sufficiently
studied in dependency parsing tasks. In an extensive empirical study, we
compare different unsupervised post-processing aggregation methods to identify
the most suitable dependency tree structure aggregation method.
