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Embeddings of words, phrases, sentences, and entire documents have several uses, one among them is to work towards interlingual representations of meaning.

Embeddings is the main subject of 19 publications.


Word embeddings have become a common feature in current research in natural language processing. Mikolov et al. (2013) propose the skip-gram method to obtain these representations. Mikolov et al. (2013) introduce efficient training methods for the skip-gram and continuous bag of words models, are used in the very popular word2vec implementation and publicly available word embedding sets for many languages.
Pennington et al. (2014) train word embedding models on the co-occurrence statistics of a word over the entire corpus.
Peters et al. (2018) demonstrate that various natural language tasks can be improved by contextualizing word embeddings through bi-directional neural language model layers, just as it is done in encoders in machine translations.
Xing et al. (2015) point out inconsistencies in the representation of word embeddings and the objective function for translation transforms between word embeddings, which they address with normalization.

Phrase Embeddings:

Zhang et al. (2014) learn phrase embeddings using recursive neural networks and auto-encoders and a mapping between input and output phrase to add an additional score to the phrase translations and to filter the phrase table. Hu et al. (2015) use convolutional neural networks to encode the input and output phrase and pass them to matching that computes their similarity. They include the full input sentence context in the and use a learning strategy called curriculum learning that first learns from the easy training examples and then the harder ones.



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