Køpsala: Transition-Based Graph Parsing via Efficient Training and Effective Encoding
Table 1: LAS on the combined dev set for udpipe models trained on the language’s combined training treebanks and the models trained on the language’s largest treebank. No consistent trend is observed.
['[EMPTY]', '[BOLD] Language Czech', '[BOLD] Language Dutch', '[BOLD] Language Estonian', '[BOLD] Language Polish']
Preprocessing model selection. Since the dev and test data do not denote their source treebanks, we simply process the text using the pipeline model trained on the language’s largest treebank. To experiment with an alternative method, for languages with more than one treebank, we also train udpipe models on combined training treebanks. The results show that using the combined training sets does not lead to consistent improvements in terms of LAS, and so we continue using pre-trained treebank-specific preprocessing models henceforth.