Global Greedy Dependency Parsing
Table 1: Comparison of results on the test sets. “T”, “G” and “E” indicate transition-based, graph-based and easy-first models, respectively. The “G + E” represents the graph-based training while the easy-first algorithm is used for inference. Acronyms used: (g) – greedy, (b) – beam search, (re) – re-ranking, (3rd) – 3rd-order, (1st) – 1st-order. The “*” in the upper right corner of the results is because the original ELMo [Peters et al.2018] has no Chinese version. We instead used the multilingual version “HIT-ELMo” pre-trained by [Che et al.2018].
['[BOLD] System', '[BOLD] Method', '[BOLD] PTB-SD LAS(%)', '[BOLD] PTB-SD UAS(%)', '[BOLD] CTB LAS(%)', '[BOLD] CTB UAS(%)']
(HT-LSTM) on both PTB and CTB. Compared with other graph- and transition-based parsers, our model is also competitive with the state-of-the-art on PTB when considering the UAS metric. Compared to state-of-the-art parsers in transition and graph types, BIAF and STACKPTR, respectively, our model gives better or comparable results but with much faster training and decoding. Additionally, with the help of pre-trained language models, ELMo or BERT, our model can achieve even greater results.