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        <title>A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors</title>
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        <namePart type="given">Mikhail</namePart>
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        <namePart type="family">Saunshi</namePart>
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    <name type="personal">
        <namePart type="given">Yingyu</namePart>
        <namePart type="family">Liang</namePart>
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        <namePart type="given">Tengyu</namePart>
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    <name type="personal">
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    <name type="personal">
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    <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.</abstract>
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%0 Conference Proceedings
%T A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors
%A Khodak, Mikhail
%A Saunshi, Nikunj
%A Liang, Yingyu
%A Ma, Tengyu
%A Stewart, Brandon
%A Arora, Sanjeev
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F khodak-etal-2018-la
%X 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.
%R 10.18653/v1/P18-1002
%U https://aclanthology.org/P18-1002
%U https://doi.org/10.18653/v1/P18-1002
%P 12-22

Markdown (Informal)

[A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors](https://aclanthology.org/P18-1002) (Khodak et al., ACL 2018)

• A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors (Khodak et al., ACL 2018)

ACL

• Mikhail Khodak, Nikunj Saunshi, Yingyu Liang, Tengyu Ma, Brandon Stewart, and Sanjeev Arora. 2018. A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 12–22, Melbourne, Australia. Association for Computational Linguistics.