Extracting Headless MWEs from Dependency Parse Trees:Parsing, Tagging, and Joint Modeling Approaches
Table A1: Comparison of our (non-MTL) parsing models with the best-performing systems (Che et al., 2018; Qi et al., 2018) from the CoNLL 2018 shared task, measured by labeled attachment scores (LAS, %).
['Treebank', 'Our', 'CoNLL 2018']
To confirm that we work with reasonable parsing models, we compare our parsers with those in the CoNLL 2018 shared task Zeman et al. The shared task featured an end-to-end parsing task, requiring all levels of text processing including tokenization, POS tagging, morphological analysis, etc. We focus on the parsing task only, and predict syntactic trees based on sentences tokenized by the Qi et al. Indeed, on four out of the six treebanks we selected for their density of flat structures, our baseline models actually achieve higher labeled attachment scores (LAS) than the the top scorer did in the official shared task.