  While large-scale pretrained language models have been shown to learn
effective linguistic representations for many NLP tasks, there remain many
real-world contextual aspects of language that current approaches do not
capture. For instance, consider a cloze-test "I enjoyed the ____ game this
weekend": the correct answer depends heavily on where the speaker is from, when
the utterance occurred, and the speaker's broader social milieu and
preferences. Although language depends heavily on the geographical, temporal,
and other social contexts of the speaker, these elements have not been
incorporated into modern transformer-based language models. We propose a simple
but effective approach to incorporate speaker social context into the learned
representations of large-scale language models. Our method first learns dense
representations of social contexts using graph representation learning
algorithms and then primes language model pretraining with these social context
representations. We evaluate our approach on geographically-sensitive
language-modeling tasks and show a substantial improvement (more than 100%
relative lift on MRR) compared to baselines.
