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Relation extraction is essentially a text classification problem, which canbe tackled by fine-tuning a pre-trained language model (LM). However, a keychallenge arises from the fact that relation extraction cannotstraightforwardly be reduced to sequence or token classification. Existingapproaches therefore solve the problem in an indirect way: they fine-tune an LMto learn embeddings of the head and tail entities, and then predict therelationship from these entity embeddings. Our hypothesis in this paper is thatrelation extraction models can be improved by capturing relationships in a moredirect way. In particular, we experiment with appending a prompt with a [MASK]token, whose contextualised representation is treated as a relation embedding.While, on its own, this strategy significantly underperforms the aforementionedapproach, we find that the resulting relation embeddings are highlycomplementary to what is captured by embeddings of the head and tail entity. Byjointly considering both types of representations, we end up with a simplemodel that outperforms the state-of-the-art across several relation extractionbenchmarks.

Entity or Relation Embeddings? An Analysis of Encoding Strategies for Relation Extraction