  Though language model text embeddings have revolutionized NLP research, their
ability to capture high-level semantic information, such as relations between
entities in text, is limited. In this paper, we propose a novel contrastive
learning framework that trains sentence embeddings to encode the relations in a
graph structure. Given a sentence (unstructured text) and its graph, we use
contrastive learning to impose relation-related structure on the token-level
representations of the sentence obtained with a CharacterBERT (El Boukkouri et
al.,2020) model. The resulting relation-aware sentence embeddings achieve
state-of-the-art results on the relation extraction task using only a simple
KNN classifier, thereby demonstrating the success of the proposed method.
Additional visualization by a tSNE analysis shows the effectiveness of the
learned representation space compared to baselines. Furthermore, we show that
we can learn a different space for named entity recognition, again using a
contrastive learning objective, and demonstrate how to successfully combine
both representation spaces in an entity-relation task.
