A Simple LSTM model for Transition-based Dependency Parsing
Table 2: Final dev and test set scores on WSJ (SD). zhang2015top do not use pre-trained word vectors for their final result. The values given for andor2016globally and weiss2015structured reflect only the performance of the greedy FFN models produced in their work, with other improvements made explained breifly in section 1. C & M refers to Chen & Manning, K & G refers to Kiperwasser & Goldberg.
['[BOLD] Network Type', '[BOLD] Dev UAS', '[BOLD] Dev LAS', '[BOLD] Test UAS', '[BOLD] Test LAS']
This is in addition to achieving very close dev score accuracy results with only a single 256 neuron hidden layer when compared to the significantly larger models of \newciteweiss2015structured with 2 layers of size 2048, and \newciteandor2016globally with 2 layers of size 1024 layers. This is also true of the LSTM-based models, where the strength of their contributions lies in their innovative approaches to feature extraction while keeping the rest of the network essentially the same. The Elman network is stronger than expected and benefits greatly from this approach.