Using a feedforward NN and embedding features, TUPA MLP obtains higher scores than TUPA Sparse , but is outperformed by the LSTM parser on primary edges.
using better input encoding allowing virtual look-ahead and look-behind in the token representation, TUPA BiLSTM obtains substantially higher scores than TUPA MLP and all other parsers, on both primary and remote edges, both in the in-domain and out-of-domain settings.