  Relation Extraction (RE) remains a challenging task, especially when
considering realistic out-of-domain evaluations. One of the main reasons for
this is the limited training size of current RE datasets: obtaining
high-quality (manually annotated) data is extremely expensive and cannot
realistically be repeated for each new domain. An intermediate training step on
data from related tasks has shown to be beneficial across many NLP
tasks.However, this setup still requires supplementary annotated data, which is
often not available. In this paper, we investigate intermediate pre-training
specifically for RE. We exploit the affinity between syntactic structure and
semantic RE, and identify the syntactic relations which are closely related to
RE by being on the shortest dependency path between two entities. We then take
advantage of the high accuracy of current syntactic parsers in order to
automatically obtain large amounts of low-cost pre-training data. By
pre-training our RE model on the relevant syntactic relations, we are able to
outperform the baseline in five out of six cross-domain setups, without any
additional annotated data.
