We present the results of our study into the ability of state-of-the-art first-order neural graphbased parsers, with seemingly simple architectures, to explicitly forego structured learning and prediction.
1 this is not due to a significantly faster, simpler, algorithm for projective maximum spanning tree (MST) decoding than Eisner (1996)'s algorithm, which we formally prove to be impossible, given the Exponential Time Hypothesis.