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 [BibTeX] [Marc21]
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.
Projects Idiap
Authors Theodoropoulos, Christos
Henderson, James
Coman, Andrei Catalin
Moens, Marie-Francine
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