A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors (P18-1)
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| Challenge: | Existing word2vec-based methods for learning rare or unseen words have been criticized for degrading performance in small corpus settings. |
| Approach: | They propose a la carte embedding method that relies on a linear transformation that is efficiently learnable using pretrained word vectors and linear regression. |
| Outcome: | The proposed method is based on a new dataset showing that it can be used when a word is encountered even if only a single usage example is available. |
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