Papers › A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors
A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors
Mikhail Khodak, Nikunj Saunshi, YIngyu Liang, Tengyu Ma, Brandon Stewart, Sanjeev Arora
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.
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Syntology Ran 2 of 3 code samples harvested from 1 repository linked to this paper; 1 has no recorded run. Of those that ran: 1 ran · honoured contract; 1 ran · violated contract.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Sentiment Analysis | CR | byte mLSTM7 | Accuracy | 90.6 | #5 of 9 | Archive leaderboard | report |
| Sentiment Analysis | MPQA | byte mLSTM7 | Accuracy | 88.8 | #3 of 4 | Archive leaderboard | report |
| Sentiment Analysis | MR | byte mLSTM7 | Accuracy | 86.8 | #4 of 19 | Archive leaderboard | report |
| Sentiment Analysis | SST-2 Binary classification | byte mLSTM7 | Accuracy | 91.7 | #49 of 87 | Archive leaderboard | report |
| Sentiment Analysis | SST-5 Fine-grained classification | byte mLSTM7 | Accuracy | 54.6 | #9 of 31 | Archive leaderboard | report |
| Subjectivity Analysis | SUBJ | byte mLSTM7 | Accuracy | 94.7 | #8 of 19 | Archive leaderboard | report |
| Text Classification | TREC-6 | byte mLSTM7 | Error | 9.6 | #18 of 19 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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