  In this paper, we focus on the problem of adapting word vector-based models
to new textual data. Given a model pre-trained on large reference data, how can
we adapt it to a smaller piece of data with a slightly different language
distribution? We frame the adaptation problem as a monolingual word vector
alignment problem, and simply average models after alignment. We align vectors
using the RCSLS criterion. Our formulation results in a simple and efficient
algorithm that allows adapting general-purpose models to changing word
distributions. In our evaluation, we consider applications to word embedding
and text classification models. We show that the proposed approach yields good
performance in all setups and outperforms a baseline consisting in fine-tuning
the model on new data.
