mlsanityData to 14 Oct 2026

Endowing Language Models with Multimodal Knowledge Graph Representations

arXiv 2206.13163 ↗First posted 27 Jun 2022cs.CL cs.AIcited by 11

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Abstract (arXiv)

We propose a method to make natural language understanding models more parameter efficient by storing knowledge in an external knowledge graph (KG) and retrieving from this KG using a dense index. Given (possibly multilingual) downstream task data, e.g., sentences in German, we retrieve entities from the KG and use their multimodal representations to improve downstream task performance. We use the recently released VisualSem KG as our external knowledge repository, which covers a subset of Wikipedia and WordNet entities, and compare a mix of tuple-based and graph-based algorithms to learn entity and relation representations that are grounded on the KG multimodal information. We demonstrate the usefulness of the learned entity representations on two downstream tasks, and show improved performance on the multilingual named entity recognition task by 0.3%--0.7% F1, while we achieve up to 2.5% improvement in accuracy on the visual sense disambiguation task. All our code and data are available in: urlhttps://github.com/iacercalixto/visualsem-kg.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 34 of its 48 references that are in the map. First posted on arXiv 27 Jun 2022.

Builds on

34 of 48 references.

References · 34

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22 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 2; 2023: 3; 2024: 4; 2025: 1; 2026: 1.

11 papers.