Sage-Tips

What are the differences between ELMo and BERT?

BERT is different from ELMo and company primarily because it targets a different training objective. Even ELMo, which uses a bidirectional LSTM, simply concatenated the left-to-right and right-to-left information, meaning that the representation couldn’t take advantage of both left and right contexts simultaneously.

What is the difference between BERT and Word2Vec?

Word2Vec will generate the same single vector for the word bank for both the sentences. Whereas, BERT will generate two different vectors for the word bank being used in two different contexts. One vector will be similar to words like money, cash etc. The other vector would be similar to vectors like beach, coast etc.

What is the difference between GloVe and Word2Vec?

Glove model is based on leveraging global word to word co-occurance counts leveraging the entire corpus. Word2vec on the other hand leverages co-occurance within local context (neighbouring words).

How is ELMo different from Word2vec?

Glove and Word2vec are word based models – that is the models take as input words and output word embeddings. Elmo in contrast is a character based model using character convolutions and can handle out of vocabulary words for this reason.

Does BERT use Word2vec?

BERT does not provide word-level representation. It provides sub-words embeddings and sentence representations. For some words, there may be a single subword while, for others, the word may be decomposed in multiple subwords.

Does BERT have word embeddings?

As discussed, BERT base model uses 12 layers of transformer encoders, each output per token from each layer of these can be used as a word embedding!

How is ELMo different from Word2Vec?

What are ELMo embeddings?

ELMo (“Embeddings from Language Model”) is a word embedding method for representing a sequence of words as a corresponding sequence of vectors. Character-level tokens are taken as the inputs to a bi-directional LSTM which produces word-level embeddings.

How does the BERT embedding differ from Word2vec FastText and GloVe?

The main difference above is a consequence of the fact Word2vec and Glove do not take into account word order in their training – ELMo and BERT take into account word order (ELMo uses LSTMS; BERT uses Transformer – an attention based model with positional encodings to represent word positions).