@inproceedings{choudhary-oriordan-2021-end,
    title = "End-to-end m{BERT} based Seq2seq Enhanced Dependency Parser with Linguistic Typology knowledge",
    author = "Choudhary, Chinmay  and
      O{'}riordan, Colm",
    editor = "Oepen, Stephan  and
      Sagae, Kenji  and
      Tsarfaty, Reut  and
      Bouma, Gosse  and
      Seddah, Djam{\'e}  and
      Zeman, Daniel",
    booktitle = "Proceedings of the 17th International Conference on Parsing Technologies and the IWPT 2021 Shared Task on Parsing into Enhanced Universal Dependencies (IWPT 2021)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2021.iwpt-1.24/",
    doi = "10.18653/v1/2021.iwpt-1.24",
    pages = "225--232",
    abstract = "We describe the NUIG solution for IWPT 2021 Shared Task of Enhanced Dependency (ED) parsing in multiple languages. For this shared task, we propose and evaluate an End-to-end Seq2seq mBERT-based ED parser which predicts the ED-parse tree of a given input sentence as a relative head-position tag-sequence. Our proposed model is a multitasking neural-network which performs five key tasks simultaneously namely UPOS tagging, UFeat tagging, Lemmatization, Dependency-parsing and ED-parsing. Furthermore we utilise the linguistic typology available in the WALS database to improve the ability of our proposed end-to-end parser to transfer across languages. Results show that our proposed Seq2seq ED-parser performs on par with state-of-the-art ED-parser despite having a much simpler de- sign."
}