| 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. |
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| Challenge: | Prior work relies on discrete citation relations to generate contrast samples, but discrete ones enforce a hard cut-off to similarity. |
| Approach: | They propose to use nearest neighbor sampling to learn continuous similarity and to sample hard-to-learn negatives and positives by controlling the sampling margin between them. |
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A Mixture-of-Experts Model for Learning Multi-Facet Entity Embeddings (2020.coling-main)
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| Challenge: | Existing methods for learning entity embeddings from text descriptions leave it to downstream applications to identify these different facets and to select the most relevant ones. |
| Approach: | They propose a model that instead learns several vectors for each entity, each of which captures a different aspect of the considered domain. |
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Self-Discriminative Learning for Unsupervised Document Embedding (N19-1)
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| Challenge: | Existing methods for document embedding learning do not consider inter-document relationships. |
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Multi-View Document Representation Learning for Open-Domain Dense Retrieval (2022.acl-long)
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Learning Domain-Sensitive and Sentiment-Aware Word Embeddings (P18-1)
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| Challenge: | Existing word embeddings cannot produce domain-sensitive embeddables due to domain-specific nature of words. |
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A Simple Approach to Learning Unsupervised Multilingual Embeddings (2020.emnlp-main)
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| Challenge: | Recent work on unsupervised cross-lingual embeddings in the bilingual setting has given the impetus to learning a shared embeddable space for several languages. |
| Approach: | They propose to solve two sub-problems together to learn a shared embedding space for several languages. |
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Are the Best Multilingual Document Embeddings simply Based on Sentence Embeddings? (2023.findings-eacl)
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| Challenge: | obtaining document embeddings at document level is challenging due to computational requirements and lack of appropriate data. |
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KIT-Multi: A Translation-Oriented Multilingual Embedding Corpus (L18-1)
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| Challenge: | Cross-lingual word embeddings are representations of words across languages in a shared continuous vector space. |
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Shallow Domain Adaptive Embeddings for Sentiment Analysis (D19-1)
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| Challenge: | Existing domain adaptation algorithms for text classification are limited by lack of training data and exploiting domain idiosyncrasies to improve performance. |
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Grouping Entities with Shared Properties using Multi-Facet Prompting and Property Embeddings (2025.emnlp-main)
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| Challenge: | Methods for learning taxonomies from data are well-studied, but it is difficult to use them in large domains. |
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