  This paper describes the proposed system of the Hitachi team for the
Cross-Framework Meaning Representation Parsing (MRP 2019) shared task. In this
shared task, the participating systems were asked to predict nodes, edges and
their attributes for five frameworks, each with different order of
"abstraction" from input tokens. We proposed a unified encoder-to-biaffine
network for all five frameworks, which effectively incorporates a shared
encoder to extract rich input features, decoder networks to generate anchorless
nodes in UCCA and AMR, and biaffine networks to predict edges. Our system was
ranked fifth with the macro-averaged MRP F1 score of 0.7604, and outperformed
the baseline unified transition-based MRP. Furthermore, post-evaluation
experiments showed that we can boost the performance of the proposed system by
incorporating multi-task learning, whereas the baseline could not. These imply
efficacy of incorporating the biaffine network to the shared architecture for
MRP and that learning heterogeneous meaning representations at once can boost
the system performance.
