  This paper describes the HUJI-KU system submission to the shared task on
Cross-Framework Meaning Representation Parsing (MRP) at the 2020 Conference for
Computational Language Learning (CoNLL), employing TUPA and the HIT-SCIR
parser, which were, respectively, the baseline system and winning system in the
2019 MRP shared task. Both are transition-based parsers using BERT
contextualized embeddings. We generalized TUPA to support the newly-added MRP
frameworks and languages, and experimented with multitask learning with the
HIT-SCIR parser. We reached 4th place in both the cross-framework and
cross-lingual tracks.
