@inproceedings{arakelyan-etal-2018-towards,
    title = "Towards {J}oint{UD}: Part-of-speech Tagging and Lemmatization using Recurrent Neural Networks",
    author = "Arakelyan, Gor  and
      Hambardzumyan, Karen  and
      Khachatrian, Hrant",
    editor = "Zeman, Daniel  and
      Haji{\v{c}}, Jan",
    booktitle = "Proceedings of the {C}o{NLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/K18-2018/",
    doi = "10.18653/v1/K18-2018",
    pages = "180--186",
    abstract = "This paper describes our submission to CoNLL UD Shared Task 2018. We have extended an LSTM-based neural network designed for sequence tagging to additionally generate character-level sequences. The network was jointly trained to produce lemmas, part-of-speech tags and morphological features. Sentence segmentation, tokenization and dependency parsing were handled by UDPipe 1.2 baseline. The results demonstrate the viability of the proposed multitask architecture, although its performance still remains far from state-of-the-art."
}