  We introduce entity post-modifier generation as an instance of a
collaborative writing task. Given a sentence about a target entity, the task is
to automatically generate a post-modifier phrase that provides contextually
relevant information about the entity. For example, for the sentence, "Barack
Obama, _______, supported the #MeToo movement.", the phrase "a father of two
girls" is a contextually relevant post-modifier. To this end, we build PoMo, a
post-modifier dataset created automatically from news articles reflecting a
journalistic need for incorporating entity information that is relevant to a
particular news event. PoMo consists of more than 231K sentences with
post-modifiers and associated facts extracted from Wikidata for around 57K
unique entities. We use crowdsourcing to show that modeling contextual
relevance is necessary for accurate post-modifier generation. We adapt a number
of existing generation approaches as baselines for this dataset. Our results
show there is large room for improvement in terms of both identifying relevant
facts to include (knowing which claims are relevant gives a >20% improvement in
BLEU score), and generating appropriate post-modifier text for the context
(providing relevant claims is not sufficient for accurate generation). We
conduct an error analysis that suggests promising directions for future
research.
