Before revision:  We find that  pre-trained embeddings have by far the greatest and most clear-cut impact on parser performance . The choice of factorized vs. unfactorized architectures and a multi-task training setup affect parsing accuracy in more subtle ways, depending on target language and output representation (trees vs. graphs).
After revision:  We find that pre-trained embeddings have by far the greatest and most clear-cut impact on parser performance and identify XLM-R as a robust choice across the languages in our study. Adding LSTM layers provides no benefits. A multi-task training setup affect parsing accuracy in more subtle ways, depending on target language and output representation (trees vs. graphs).