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Unlike static word embeddings, these embeddings are at the token-level, in that each occurrence of a word has its own embedding. These embeddings better reflect the multi-sense nature of words, because occurrences of a word in similar contexts are situated in similar regions of BERT’s embedding space. [41] [42]
The reasons for successful word embedding learning in the word2vec framework are poorly understood. Goldberg and Levy point out that the word2vec objective function causes words that occur in similar contexts to have similar embeddings (as measured by cosine similarity) and note that this is in line with J. R. Firth's distributional hypothesis ...
ELMo (embeddings from language model) is a word embedding method for representing a sequence of words as a corresponding sequence of vectors. [1] It was created by researchers at the Allen Institute for Artificial Intelligence , [ 2 ] and University of Washington and first released in February, 2018.
Whereas an embedding layer converts a token into a vector, an un-embedding layer converts a vector into a probability distribution over tokens. The un-embedding layer is a linear- softmax layer: U n E m b e d ( x ) = s o f t m a x ( x W + b ) {\displaystyle \mathrm {UnEmbed} (x)=\mathrm {softmax} (xW+b)} The matrix has shape ( d emb , n ...
The bag-of-words model (BoW) is a model of text which uses an unordered collection (a "bag") of words. It is used in natural language processing and information retrieval (IR). It disregards word order (and thus most of syntax or grammar) but captures multiplicity .
static is a reserved word in many programming languages to modify a declaration. The effect of the keyword varies depending on the details of the specific programming language, most commonly used to modify the lifetime (as a static variable) and visibility (depending on linkage), or to specify a class member instead of an instance member in classes.
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In practice however, BERT's sentence embedding with the [CLS] token achieves poor performance, often worse than simply averaging non-contextual word embeddings. SBERT later achieved superior sentence embedding performance [8] by fine tuning BERT's [CLS] token embeddings through the usage of a siamese neural network architecture on the SNLI dataset.