What's New in Deep Learning Research: Facebook Meta-Embeddings Allow NLP Models to Choose Their…

Word embeddings have revolutionized the world of natural language processing(NLP). Conceptually, word embeddings are language modeling methods that map phrases or words in a sentence to vectors and numbers. One of the first steps in any NLP application is to determine what type of word embedding algorithm is going to be used. Typically, NLP models resort to pretrained word embedding algorithm such as Word2Vec, Glove or FastText. While that approach is relatively simple, it also results highly inefficient as is near to impossible to determine what word embedding will perform better as the NLP model evolves.