Imposing Relation Structure in Language-Model Embeddings Using Contrastive Learning
| Type of publication: | Conference paper |
| Citation: | Theodoropoulos_CONLL2021_2021 |
| Publication status: | Published |
| Booktitle: | Proceedings of the 25th Conference on Computational Natural Language Learning |
| Year: | 2021 |
| Month: | November |
| Pages: | 337-348 |
| Publisher: | Association for Computational Linguistics |
| Location: | Online |
| Abstract: | 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. |
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| Added by: | [UNK] |
| Total mark: | 0 |
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