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HUJI-KU at MRP~2020: Two Transition-based Neural Parsers

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arxiv 2010.05710 v1 pith:IT53LKJK submitted 2020-10-12 cs.CL cs.LG

Ofir Arviv , Ruixiang Cui , Daniel Hershcovich This is my paper · ORCID
classification cs.CLcs.LG
keywords systemcross-frameworkhit-scirhuji-kulearningparserparsersshared
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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.

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