Recently, such techniques were used to build state-of-the-art syntactic parsers, and have demonstrated the effectiveness of deep neural architectures for decision making in lineartime dependency parsing (Chen and Manning, 2014;Dyer et al., 2015;Andor et al., 2016;Kiperwasser and Goldberg, 2016).
we set the number of epochs T = 3, regularization coefficient 2 = 10 −6 , learning rate 10 http://deeplearning4j.org/ Parser P R F CKY (Artzi et al., 2015) 67   µ = 0.05, skipping term γ = 1.0.