This work indicates that language translation is vector addition on a lower dimensional semantic manifold that neural networks converge on, derived from the hig

This work indicates that language translation is vector addition on a lower dimensional semantic manifold that neural networks converge on, derived from the high dimensional whole vocabulary context vectors of the input. It is extended by : Sequence to Sequence Learning with Neural Networks by Sutskever, Vinyals & Le http://arxiv.org/abs/1409.3215 http://arxiv.org/abs/1409.3215 which adds LTSM, a trainable deep neural net type of long term memory - which allows whole sentences to be digested and translated. " Our main result is that on an English to French translation task from the WMT'14 dataset, the translations produced by the LSTM achieve a BLEU score of 34.8 on the entire test set, ... Additionally, the LSTM did not have difficulty on long sentences."[1] another interesting result is the translation of pixels to words that describe the image. Deep Fragment Embeddings for Bidirectional Image Sentence Mapping by Karpathy, Joulin, & Li http://papers.nips.cc/paper/5281-deep-fragment-embeddings-for-bidirectional-image-sentence-mapping http://papers.nips.cc/paper/5281-deep-fragment-embeddings-fo... code & a demo is available : http://cs.stanford.edu/people/karpathy/deepimagesent/ http://cs.stanford.edu/people/karpathy/deepimagesent/