@inproceedings{cavar-etal-2024-typology,
    title = "The Typology of Ellipsis: A Corpus for Linguistic Analysis and Machine Learning Applications",
    author = "Cavar, Damir  and
      Mompelat, Ludovic  and
      Abdo, Muhammad",
    editor = "Hahn, Michael  and
      Sorokin, Alexey  and
      Kumar, Ritesh  and
      Shcherbakov, Andreas  and
      Otmakhova, Yulia  and
      Yang, Jinrui  and
      Serikov, Oleg  and
      Rani, Priya  and
      Ponti, Edoardo M.  and
      Murado{\u{g}}lu, Saliha  and
      Gao, Rena  and
      Cotterell, Ryan  and
      Vylomova, Ekaterina",
    booktitle = "Proceedings of the 6th Workshop on Research in Computational Linguistic Typology and Multilingual NLP",
    month = mar,
    year = "2024",
    address = "St. Julian's, Malta",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2024.sigtyp-1.6/",
    pages = "46--54",
    abstract = "State-of-the-art (SotA) Natural Language Processing (NLP) technology faces significant challenges with constructions that contain ellipses. Although theoretically well-documented and understood, there needs to be more sufficient cross-linguistic language resources to document, study, and ultimately engineer NLP solutions that can adequately provide analyses for ellipsis constructions. This article describes the typological data set on ellipsis that we created for currently seventeen languages. We demonstrate how SotA parsers based on a variety of syntactic frameworks fail to parse sentences with ellipsis, and in fact, probabilistic, neural, and Large Language Models (LLM) do so, too. We demonstrate experiments that focus on detecting sentences with ellipsis, predicting the position of elided elements, and predicting elided surface forms in the appropriate positions. We show that cross-linguistic variation of ellipsis-related phenomena has different consequences for the architecture of NLP systems."
}Markdown (Informal)
[The Typology of Ellipsis: A Corpus for Linguistic Analysis and Machine Learning Applications](https://preview.aclanthology.org/ingest-emnlp/2024.sigtyp-1.6/) (Cavar et al., SIGTYP 2024)
ACL