@inproceedings{khodak-etal-2018-la,
    title = "A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors",
    author = "Khodak, Mikhail  and
      Saunshi, Nikunj  and
      Liang, Yingyu  and
      Ma, Tengyu  and
      Stewart, Brandon  and
      Arora, Sanjeev",
    editor = "Gurevych, Iryna  and
      Miyao, Yusuke",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P18-1002",
    doi = "10.18653/v1/P18-1002",
    pages = "12--22",
    abstract = "Motivations like domain adaptation, transfer learning, and feature learning have fueled interest in inducing embeddings for rare or unseen words, n-grams, synsets, and other textual features. This paper introduces a la carte embedding, a simple and general alternative to the usual word2vec-based approaches for building such representations that is based upon recent theoretical results for GloVe-like embeddings. Our method relies mainly on a linear transformation that is efficiently learnable using pretrained word vectors and linear regression. This transform is applicable on the fly in the future when a new text feature or rare word is encountered, even if only a single usage example is available. We introduce a new dataset showing how the a la carte method requires fewer examples of words in context to learn high-quality embeddings and we obtain state-of-the-art results on a nonce task and some unsupervised document classification tasks.",
}