Author(s): Edward Ma

Originally published on Towards AI.

Photo by Edward Ma on Unsplash

Traditional word embeddings are good at solving lots of natural language processing (NLP) downstream problems such as documentation classification and named-entity recognition (NER). However, one of the drawbacks is a lack of capability on handling out-of-vocabulary (OOV).

Facebook introduces Misspelling Oblivious (word) Embeddings (MOE) which overcomes this limitation. MOE extends fastText architecture to achieve it. Therefore, this story goes through the fastText training method and architecture before talking about MOE.

fastText extends word2vec’s architecture which uses skip-gram with negative sampling method to train a word embeddings. Skip-gram uses context words to predict surrounding words in order to… Read the full blog for free on Medium.

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Published via Towards AI