SJTU at MRP 2019: A Transition-Based Multi-Task Parser for Cross-Framework Meaning Representation Parsing
Hongxiao Bai, Hai Zhao · 2019
This paper describes the system of our team SJTU for our participation in the CoNLL 2019 Shared Task: Cross-Framework Meaning Representation Parsing.The goal of the task is to advance data-driven parsing into graphstructured representations of sentence meaning.This task includes five meaning representation frameworks: DM, PSD, EDS, UCCA, and AMR.These frameworks have different properties and structures.To tackle all the frameworks in one model, it is needed to find out the commonality of them.In our work, we define a set of the transition actions to oncefor-all tackle all the frameworks and train a transition-based model to parse the meaning representation.The adopted multi-task model also can allow learning for one framework to benefit the others.In the final official evaluation of the shared task, our system achieves 42% F 1 unified MRP metric score.