  Despite the recent progress, little is known about the features captured by
state-of-the-art neural relation extraction (RE) models. Common methods encode
the source sentence, conditioned on the entity mentions, before classifying the
relation. However, the complexity of the task makes it difficult to understand
how encoder architecture and supporting linguistic knowledge affect the
features learned by the encoder. We introduce 14 probing tasks targeting
linguistic properties relevant to RE, and we use them to study representations
learned by more than 40 different encoder architecture and linguistic feature
combinations trained on two datasets, TACRED and SemEval 2010 Task 8. We find
that the bias induced by the architecture and the inclusion of linguistic
features are clearly expressed in the probing task performance. For example,
adding contextualized word representations greatly increases performance on
probing tasks with a focus on named entity and part-of-speech information, and
yields better results in RE. In contrast, entity masking improves RE, but
considerably lowers performance on entity type related probing tasks.
