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A recent advance in monolingual dependency parsing is the idea of a treebankembedding vector, which allows all treebanks for a particular language to beused as training data while at the same time allowing the model to prefertraining data from one treebank over others and to select the preferredtreebank at test time. We build on this idea by 1) introducing a method topredict a treebank vector for sentences that do not come from a treebank usedin training, and 2) exploring what happens when we move away from predefinedtreebank embedding vectors during test time and instead devise tailoredinterpolations. We show that 1) there are interpolated vectors that aresuperior to the predefined ones, and 2) treebank vectors can be predicted withsufficient accuracy, for nine out of ten test languages, to match theperformance of an oracle approach that knows the most suitable predefinedtreebank embedding for the test set.

Treebank Embedding Vectors for Out-of-domain Dependency Parsing