  Recent work has shown that monolingual masked language models learn to
represent data-driven notions of language variation which can be used for
domain-targeted training data selection. Dataset genre labels are already
frequently available, yet remain largely unexplored in cross-lingual setups. We
harness this genre metadata as a weak supervision signal for targeted data
selection in zero-shot dependency parsing. Specifically, we project
treebank-level genre information to the finer-grained sentence level, with the
goal to amplify information implicitly stored in unsupervised contextualized
representations. We demonstrate that genre is recoverable from multilingual
contextual embeddings and that it provides an effective signal for training
data selection in cross-lingual, zero-shot scenarios. For 12 low-resource
language treebanks, six of which are test-only, our genre-specific methods
significantly outperform competitive baselines as well as recent
embedding-based methods for data selection. Moreover, genre-based data
selection provides new state-of-the-art results for three of these target
languages.
