Arc-standard [1] is the classical algorithm in question. See section 3.2 of Dyer's paper: "Our parser is based on the arc-standard transition inventory (Nivre,

Arc-standard [1] is the classical algorithm in question. See section 3.2 of Dyer's paper: "Our parser is based on the arc-standard transition inventory (Nivre, 2004), given in Figure 3". Perhaps it's not still state of the art, but I don't see dependency parsing as a task in that suite of benchmarks, and NLP isn't my primary field, so I'm not up-to-date on the whether or not another method has outperformed it in the last three years. (I would be surprised if one hadn't.) At the time it was, and regardless, I don't doubt that the current state of the art similarly uses neural networks only as a subset of the complete method. [1] https://www.aclweb.org/anthology/W04-0308 https://www.aclweb.org/anthology/W04-0308 Edit: I can't seem to reply to the post below me. There isn't an entry CMU/Pittsburgh in your benchmark. The paper refers to the Stanford Dependency [SD] and Penn Chinese Treebank 5 [CTB5], and reports 90.9/85.7, respectively, for LAS scores. I'm not sure if any of the treebanks listed correspond to these, but these numbers do seem in line to at least be competitive regardless of which treebank is used.