  Internet links enable users to deepen their understanding of a topic by
providing convenient access to related information. However, the majority of
links are unanchored -- they link to a target webpage as a whole, and readers
may expend considerable effort localizing the specific parts of the target
webpage that enrich their understanding of the link's source context. To help
readers effectively find information in linked webpages, we introduce the task
of anchor prediction, where the goal is to identify the specific part of the
linked target webpage that is most related to the source linking context. We
release the AuthorAnchors dataset, a collection of 34K naturally-occurring
anchored links, which reflect relevance judgments by the authors of the source
article. To model reader relevance judgments, we annotate and release
ReaderAnchors, an evaluation set of anchors that readers find useful. Our
analysis shows that effective anchor prediction often requires jointly
reasoning over lengthy source and target webpages to determine their implicit
relations and identify parts of the target webpage that are related but not
redundant. We benchmark a performant T5-based ranking approach to establish
baseline performance on the task, finding ample room for improvement.
