Abstract
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.
Cite
CITATION STYLE
Arviv, O., Cui, R., & Hershcovich, D. (2020). HUJI-KU at MRP 2020: Two Transition-based Neural Parsers. In CoNLL 2020 - SIGNLL Conference on Computational Natural Language Learning, Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing (pp. 73–82). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2020.conll-shared.7
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