Viable Dependency Parsing as Sequence Labeling
Table 2: Comparison of models on the PTB test set. KG refers to TACL885, CM to chen-manning:2014:EMNLP2014 and DM to dozat-iclr. ⋄ indicates the speed is taken from their paper.
['Model', 'sent/s CPU', 'sent/s GPU', 'UAS', 'LAS']
Contrary to previous dependency-parsing-as-sequence-labeling attempts , we are competitive and provide a good speed-accuracy tradeoff. This comes in spite of its simplicity. While our BiLSTM architecture is similar to that of BIST, the sequence labeling approach does not need a stack, a specific transition system or a dynamic oracle. Using the BIST hyperparameters for our model (P2,250) yields further increases in speed, at some cost to accuracy: 3.34x faster and -0.04 LAS score than the graph-based model, and 3.51x faster and -0.94 LAS score than their transition-based one.