Universal Dependency Parsing with a General Transition-Based
Daniel Hershcovich, Omri Abend, Ari Rappoport · Proceedings of the · 2018
This paper presents our experiments with applying TUPA to the CoNLL 2018 UD shared task.TUPA is a general neural transition-based DAG parser, which we use to present the first experiments on recovering enhanced dependencies as part of the general parsing task.TUPA was designed for parsing UCCA, a crosslinguistic semantic annotation scheme, exhibiting reentrancy, discontinuity and nonterminal nodes.By converting UD trees and graphs to a UCCA-like DAG format, we train TUPA almost without modification on the UD parsing task.The generic nature of our approach lends itself naturally to multitask learning.