Papers › Enriching Word Vectors with Subword Information
Enriching Word Vectors with Subword Information
Piotr Bojanowski, Edouard Grave, Armand Joulin, Tomas Mikolov
Continuous word representations, trained on large unlabeled corpora are useful for many natural language processing tasks. Popular models that learn such representations ignore the morphology of words, by assigning a distinct vector to each word. This is a limitation, especially for languages with large vocabularies and many rare words. In this paper, we propose a new approach based on the skipgram model, where each word is represented as a bag of character n-grams. A vector representation is associated to each character n-gram; words being represented as the sum of these representations. Our method is fast, allowing to train models on large corpora quickly and allows us to compute word representations for words that did not appear in the training data. We evaluate our word representations on nine different languages, both on word similarity and analogy tasks. By comparing to recently proposed morphological word representations, we show that our vectors achieve state-of-the-art performance on these tasks.
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Code
Syntology Ran 4 of 31 code samples harvested from 7 repositories linked to this paper; 27 have no recorded run. Of those that ran: 4 ran · our draft was wrong.
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Code Syntology ran Syntology
31 samples harvested; 4 ran; 0 honoured the contract we drafted; 27 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Word Similarity | WS353 | SkipGram | Spearman's Rho | 61.0 | #3 of 3 | 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.
Methods
Introduced by this paper: fastText
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