Papers by Kamen Brestnichki

1 papers
Multilingual Factor Analysis (P19-1)

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Challenge: Existing methods for multilingual word embeddings are based on the observation that word embeds exhibit similar structures across languages.
Approach: They propose a latent variable-based model that fits a multilingual dictionary to learn multilingual word representations offline.
Outcome: The proposed model is robust to noise in the embedding space making it suitable for distributed representations learned from noisy corpora.

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