HUJI-KU at MRP 2020: Two Transition-based Neural Parsers
Ofir Arviv, Ruixiang Cui, Daniel Hershcovich · 2020
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 crossframework and cross-lingual tracks. Before Transition Transition After TransitionStack Buffer N. Edges