mlsanityData to 7 Oct 2026

Bayesian Hierarchical Words Representation Learning

arXiv 2004.07126 ↗First posted 12 Apr 2020cs.CL cs.LG stat.MLcited by 10

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Abstract (arXiv)

This paper presents the Bayesian Hierarchical Words Representation (BHWR) learning algorithm. BHWR facilitates Variational Bayes word representation learning combined with semantic taxonomy modeling via hierarchical priors. By propagating relevant information between related words, BHWR utilizes the taxonomy to improve the quality of such representations. Evaluation of several linguistic datasets demonstrates the advantages of BHWR over suitable alternatives that facilitate Bayesian modeling with or without semantic priors. Finally, we further show that BHWR produces better representations for rare words.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Apr 2020.

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Citing papers per year: 2021: 2; 2022: 3; 2023: 5.

10 papers.

  1. 2022Grad-SAM: Explaining Transformers via Gradient Self-Attention Mapscited by 42
  2. 2023Deep Integrated Explanationscited by 9
  3. 2021Cold Item Integration in Deep Hybrid Recommenders via Tunable Stochastic Gatescited by 8
  4. 2022Interpreting BERT-based Text Similarity via Activation and Saliency Mapscited by 8
  5. 2022MetricBERT: Text Representation Learning via Self-Supervised Triplet Trainingcited by 3
  6. 2023Representation Learning via Variational Bayesian Networkscited by 3
  7. 2023Efficient Discovery and Effective Evaluation of Visual Perceptual Similarity: A Benchmark and Beyondcited by 3
  8. 2023Learning to Explain: A Model-Agnostic Framework for Explaining Black Box Modelscited by 3
  9. 2021Denoising Word Embeddings by Averaging in a Shared Spacecited by 2
  10. 2023FastDoc: Domain-Specific Fast Continual Pre-training Technique using Document-Level Metadata and Taxonomycited by 0