  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.
