@inproceedings{stanovsky-tamari-2019-yall,
    title = "{Y}{'}all should read this! Identifying Plurality in Second-Person Personal Pronouns in {E}nglish Texts",
    author = "Stanovsky, Gabriel  and
      Tamari, Ronen",
    editor = "Xu, Wei  and
      Ritter, Alan  and
      Baldwin, Tim  and
      Rahimi, Afshin",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/D19-5549/",
    doi = "10.18653/v1/D19-5549",
    pages = "375--380",
    abstract = "Distinguishing between singular and plural ``you'' in English is a challenging task which has potential for downstream applications, such as machine translation or coreference resolution. While formal written English does not distinguish between these cases, other languages (such as Spanish), as well as other dialects of English (via phrases such as ``y{'}all''), do make this distinction. We make use of this to obtain distantly-supervised labels for the task on a large-scale in two domains. Following, we train a model to distinguish between the single/plural `you', finding that although in-domain training achieves reasonable accuracy ({\ensuremath{\geq}} 77{\%}), there is still a lot of room for improvement, especially in the domain-transfer scenario, which proves extremely challenging. Our code and data are publicly available."
}