Papers › Imposing Relation Structure in Language-Model Embeddings Using Contrastive Learning
Imposing Relation Structure in Language-Model Embeddings Using Contrastive Learning
Christos Theodoropoulos, James Henderson, Andrei C. Coman, Marie-Francine Moens
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.
Code
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Relation Extraction | Adverse Drug Events (ADE) Corpus | CLDR + CLNER | NER Macro F1 | 88.3 | #10 of 15 | Archive leaderboard | report |
| Relation Extraction | Adverse Drug Events (ADE) Corpus | CLDR + CLNER | RE+ Macro F1 | 79.97 | #10 of 15 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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