  Language models are at the heart of numerous works, notably in the text
mining and information retrieval communities. These statistical models aim at
extracting word distributions, from simple unigram models to recurrent
approaches with latent variables that capture subtle dependencies in texts.
However, those models are learned from word sequences only, and authors'
identities, as well as publication dates, are seldom considered. We propose a
neural model, based on recurrent language modeling, which aims at capturing
language diffusion tendencies in author communities through time. By
conditioning language models with author and temporal vector states, we are
able to leverage the latent dependencies between the text contexts. This allows
us to beat several temporal and non-temporal language baselines on two
real-world corpora, and to learn meaningful author representations that vary
through time.
