![]() Given \(T\) tokens \((x_1,x_2,\cdots,x_T)\), a forward language model computes the probability of the sequence by modeling the probability of token \(x_k\) given the history \((x_1,\cdots, x_)\). ![]() Therefore, the same word can have different word vectors under different contexts. They are computed on top of two-layer Bidirectional Language Models (biLMs) with character convolutions, as a linear function of the internal network states. Unlike most widely used word embeddings ELMo word representations are functions of the entire input sentence, instead of the single word. So even if we had a sentence like “He went to the prison cell with his cell phone to extract blood cell samples from inmates”, where the word cell has different meanings based on the sentence context, these models just collapse them all into one vector for cell in their output source. ![]() ![]() Eventhough they provided a great improvement to many NLP task, such “constant” meaning was a major drawback of this word embeddings as the meaning of words changes based on context, and thus this wasn’t the best option for Language Modelling.įor instance, after we train word2vec/Glove on a corpus we get as output one vector representation for, say the word cell. Word embeddings such as word2vec or GloVe provides an exact meaning to words. ![]()
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