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.
Topics in NeuralNetworkModelsNeural Language Models | Attention Model | Inference | Coverage | Vocabulary | Embeddings | Multilingual Word Embeddings | Monolingual Data | Adaptation | Linguistic Annotation | Multilingual Multimodal Multitask | Alternative Architectures | Analysis And Visualization | Neural Components In Statistical Machine Translation
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.