  This is a work-in-progress report, which aims to share preliminary results of
a novel sequence-to-sequence schema for dependency parsing that relies on a
combination of a BiLSTM and two Pointer Networks (Vinyals et al., 2015), in
which the final softmax function has been replaced with the logistic
regression. The two pointer networks co-operate to develop a latent syntactic
knowledge, by learning the lexical properties of "selection" and the lexical
properties of "selectability", respectively. At the moment and without
fine-tuning, the parser implementation gets a UAS of 93.14% on the English
Penn-treebank (Marcus et al., 1993) annotated with Stanford Dependencies: 2-3%
under the SOTA but yet attractive as a baseline of the approach.
