@inproceedings{verwimp-etal-2017-character,
    title = "Character-Word {LSTM} Language Models",
    author = "Verwimp, Lyan  and
      Pelemans, Joris  and
      Van hamme, Hugo  and
      Wambacq, Patrick",
    editor = "Lapata, Mirella  and
      Blunsom, Phil  and
      Koller, Alexander",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/E17-1040/",
    pages = "417--427",
    abstract = "We present a Character-Word Long Short-Term Memory Language Model which both reduces the perplexity with respect to a baseline word-level language model and reduces the number of parameters of the model. Character information can reveal structural (dis)similarities between words and can even be used when a word is out-of-vocabulary, thus improving the modeling of infrequent and unknown words. By concatenating word and character embeddings, we achieve up to 2.77{\%} relative improvement on English compared to a baseline model with a similar amount of parameters and 4.57{\%} on Dutch. Moreover, we also outperform baseline word-level models with a larger number of parameters."
}