Title: Neural Probabilistic Model for Non-projective MST Parsing,
Abstract: In this paper, we propose a probabilistic parsing model, which defines a
proper conditional probability distribution over non-projective dependency
trees for a given sentence, using neural representations as inputs. The neural
network architecture is based on bi-directional LSTM-CNNs which benefits from
both word- and character-level representations automatically, by using
combination of bidirectional LSTM and CNN. On top of the neural network, we
introduce a probabilistic structured layer, defining a conditional log-linear
model over non-projective trees. We evaluate our model on 17 different
datasets, across 14 different languages. By exploiting Kirchhoff's Matrix-Tree
Theorem (Tutte, 1984), the partition functions and marginals can be computed
efficiently, leading to a straight-forward end-to-end model training procedure
via back-propagation. Our parser achieves state-of-the-art parsing performance
on nine datasets.