  This paper explores learning rich self-supervised entity representations from
large amounts of the associated text. Once pre-trained, these models become
applicable to multiple entity-centric tasks such as ranked retrieval, knowledge
base completion, question answering, and more. Unlike other methods that
harvest self-supervision signals based merely on a local context within a
sentence, we radically expand the notion of context to include any available
text related to an entity. This enables a new class of powerful, high-capacity
representations that can ultimately distill much of the useful information
about an entity from multiple text sources, without any human supervision.
  We present several training strategies that, unlike prior approaches, learn
to jointly predict words and entities -- strategies we compare experimentally
on downstream tasks in the TV-Movies domain, such as MovieLens tag prediction
from user reviews and natural language movie search. As evidenced by results,
our models match or outperform competitive baselines, sometimes with little or
no fine-tuning, and can scale to very large corpora.
  Finally, we make our datasets and pre-trained models publicly available. This
includes Reviews2Movielens (see https://goo.gle/research-docent ), mapping the
up to 1B word corpus of Amazon movie reviews (He and McAuley, 2016) to
MovieLens tags (Harper and Konstan, 2016), as well as Reddit Movie Suggestions
(see https://urikz.github.io/docent ) with natural language queries and
corresponding community recommendations.
