Papers with Faceted Domain Encoder
Multifaceted Domain-Specific Document Embeddings (2021.naacl-demos)
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| Challenge: | Current document embeddings require large training corpora but fail to learn high-quality representations when confronted with a small number of domain-specific documents and rare terms. |
| Approach: | They propose a faceted domain encoder that transforms each document into a single embedding vector . they use a Siamese neural network architecture to leverage knowledge graphs to enhance the embeddables . |
| Outcome: | The proposed model achieves the same embedding quality as state-of-the-art models while requiring only a tiny fraction of training data. |