  Natural language is generated by people, yet traditional language modeling
views words or documents as if generated independently. Here, we propose human
language modeling (HuLM), a hierarchical extension to the language modeling
problem whereby a human-level exists to connect sequences of documents (e.g.
social media messages) and capture the notion that human language is moderated
by changing human states. We introduce, HaRT, a large-scale transformer model
for the HuLM task, pre-trained on approximately 100,000 social media users, and
demonstrate its effectiveness in terms of both language modeling (perplexity)
for social media and fine-tuning for 4 downstream tasks spanning document- and
user-levels: stance detection, sentiment classification, age estimation, and
personality assessment. Results on all tasks meet or surpass the current
state-of-the-art.
