@inproceedings{arviv-etal-2020-huji,
    title = "{HUJI}-{KU} at {MRP} 2020: Two Transition-based Neural Parsers",
    author = "Arviv, Ofir  and
      Cui, Ruixiang  and
      Hershcovich, Daniel",
    editor = "Oepen, Stephan  and
      Abend, Omri  and
      Abzianidze, Lasha  and
      Bos, Johan  and
      Haji{\v{c}}, Jan  and
      Hershcovich, Daniel  and
      Li, Bin  and
      O'Gorman, Tim  and
      Xue, Nianwen  and
      Zeman, Daniel",
    booktitle = "Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest_wac_2008/2020.conll-shared.7/",
    doi = "10.18653/v1/2020.conll-shared.7",
    pages = "73--82",
    abstract = "This paper describes the HUJI-KU system submission to the shared task on CrossFramework 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."
}