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This paper describes our system (HIT-SCIR) for the CoNLL 2019 shared task: CrossFramework Meaning Representation Parsing. We extended the basic transition-based parser with two improvements: a) Efficient Training by realizing stack LSTM parallel training; b) Effective Encoding via adopting deep contextualized word embeddings BERT (Devlin et al., 2019). Generally, we proposed a unified pipeline to meaning representation parsing, including framework-specific transitionbased parsers, BERT-enhanced word representation, and post-processing. In the final evaluation, our system was ranked first according to ALL-F1 (86.2%) and especially ranked first in UCCA framework (81.67%).

HIT-SCIR at MRP 2019: A Unified Pipeline for Meaning Representation Parsing via Efficient Training and Effective Encoding