@inproceedings{papay-etal-2018-addressing,
    title = "Addressing Low-Resource Scenarios with Character-aware Embeddings",
    author = "Papay, Sean  and
      Pad{\'o}, Sebastian  and
      Vu, Ngoc Thang",
    editor = {Faruqui, Manaal  and
      Sch{\"u}tze, Hinrich  and
      Trancoso, Isabel  and
      Tsvetkov, Yulia  and
      Yaghoobzadeh, Yadollah},
    booktitle = "Proceedings of the Second Workshop on Subword/Character {LE}vel Models",
    month = jun,
    year = "2018",
    address = "New Orleans",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/W18-1204/",
    doi = "10.18653/v1/W18-1204",
    pages = "32--37",
    abstract = "Most modern approaches to computing word embeddings assume the availability of text corpora with billions of words. In this paper, we explore a setup where only corpora with millions of words are available, and many words in any new text are out of vocabulary. This setup is both of practical interests {--} modeling the situation for specific domains and low-resource languages {--} and of psycholinguistic interest, since it corresponds much more closely to the actual experiences and challenges of human language learning and use. We compare standard skip-gram word embeddings with character-based embeddings on word relatedness prediction. Skip-grams excel on large corpora, while character-based embeddings do well on small corpora generally and rare and complex words specifically. The models can be combined easily."
}