A Span-based Linearization for Constituent Trees
Table 6: Parsing speeds on the PTB test set. {}^{\spadesuit} means the models use Transformer as their encoders. “w. Cython” stands for using Cython to optimize the python code. “w/o tree inference” stands for evaluating without tree inference. The model in DBLP:conf/acl/KleinK18 is ran by ourselves, and other speeds are extracted from their original papers.
['Model', 'sents/sec']
Although our inference complexity is \mathcal{O}(n\log n), our speed is faster than other local models, except DBLP:conf/acl/BengioSCJLS18 which evaluates without tree inference and DBLP:conf/naacl/VilaresAS19 which utilizes a pure sequence tagging framework. This is mainly due to the simplicity of our model and the parallelism of matrix operations for structure prediction. Compared with globally normalized parsers like DBLP:conf/acl/ZhouZ19 and DBLP: conf/acl/KleinK18, our model is also faster even if they use optimization for python code Other global model like DBLP: conf/acl/SternAK17 which infers in O(n^{3}) complexity is much slower than ours, and this shows the superiority of our linearization in speed.