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What are word embeddings, wordpiece and language-agnostic bert?


Asked by Harper Lamb on Dec 06, 2021 FAQ



WordPiece Embeddings in BERT Word Piece embeddings was developed for google speech recognition system for Asian languages like Korean and Japanese. These languages have large inventory of characters, homonyms and no or few spaces between words. No or fewer spaces meant segmentation was necessary for the text.
One may also ask,
BERT uses WordPiece Embeddings of 30,000 token vocabulary. The first token of every sequence is always a special classification token ([CLS]). The final hidden state corresponding to this token is used as the aggregate sequence representation for classification tasks. Sentence pairs are packed together into a single sequence.
And, BERT offers an advantage over models like Word2Vec, because while each word has a fixed representation under Word2Vec regardless of the context within which the word appears, BERT produces word representations that are dynamically informed by the words around them. For example, given two sentences:
In respect to this,
BERT brought everything together to build a bidirectional transformer-based language model using encoders rather than decoders! To overcome the “see itself” issue, the guys at Google had an ingenious idea. They employed masked language modeling. In other words, they hid 15% of the words and used their position information to infer them.
Accordingly,
Segment Embeddings: BERT can also take sentence pairs as inputs for tasks (Question-Answering). That’s why it learns a unique embedding for the first and the second sentences to help the model distinguish between them. In the above example, all the tokens marked as EA belong to sentence A (and similarly for EB)