Transferring Neural Potentials For High Order Dependency Parsing
Farshad Noravesh
High order dependency parsing leverages high order features such as siblings
or grandchildren to improve state of the art accuracy of current first order
dependency parsers. The present paper uses biaffine scores to provide an
estimate of the arc scores and is then propagated into a graphical model. The
inference inside the graphical model is solved using dual decomposition. The
present algorithm propagates biaffine neural scores to the graphical model and
by leveraging dual decomposition inference, the overall circuit is trained
end-to-end to transfer first order informations to the high order informations.