@article{more-etal-2019-joint,
    title = "Joint Transition-Based Models for Morpho-Syntactic Parsing: Parsing Strategies for {MRL}s and a Case Study from {M}odern {H}ebrew",
    author = "More, Amir  and
      Seker, Amit  and
      Basmova, Victoria  and
      Tsarfaty, Reut",
    editor = "Lee, Lillian  and
      Johnson, Mark  and
      Roark, Brian  and
      Nenkova, Ani",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    year = "2019",
    address = "Cambridge, MA",
    publisher = "MIT Press",
    url = "https://preview.aclanthology.org/ingest-emnlp/Q19-1003/",
    doi = "10.1162/tacl_a_00253",
    pages = "33--48",
    abstract = "In standard NLP pipelines, morphological analysis and disambiguation (MA{\&}D) precedes syntactic and semantic downstream tasks. However, for languages with complex and ambiguous word-internal structure, known as morphologically rich languages (MRLs), it has been hypothesized that syntactic context may be crucial for accurate MA{\&}D, and vice versa. In this work we empirically confirm this hypothesis for Modern Hebrew, an MRL with complex morphology and severe word-level ambiguity, in a novel transition-based framework. Specifically, we propose a joint morphosyntactic transition-based framework which formally unifies two distinct transition systems, morphological and syntactic, into a single transition-based system with joint training and joint inference. We empirically show that MA{\&}D results obtained in the joint settings outperform MA{\&}D results obtained by the respective standalone components, and that end-to-end parsing results obtained by our joint system present a new state of the art for Hebrew dependency parsing."
}