  We first present our work in machine translation, during which we used
aligned sentences to train a neural network to embed n-grams of different
languages into an $d$-dimensional space, such that n-grams that are the
translation of each other are close with respect to some metric. Good n-grams
to n-grams translation results were achieved, but full sentences translation is
still problematic. We realized that learning semantics of sentences and
documents was the key for solving a lot of natural language processing
problems, and thus moved to the second part of our work: sentence compression.
We introduce a flexible neural network architecture for learning embeddings of
words and sentences that extract their semantics, propose an efficient
implementation in the Torch framework and present embedding results comparable
to the ones obtained with classical neural language models, while being more
powerful.
