Abstract

We introduce EigenNoise, a straightforward initialization scheme for word vectors based on a theoretical model of dense, independent co-occurrence. The paper presents preliminary results showing that this data-free method, when fine-tuned, can approach the performance of empirically trained GloVe vectors. This suggests EigenNoise is a promising warm-start technique, especially in settings with limited data or computational resources.

Key Contributions

  • Data-Free Initialization: Proposes a “naïve” initialization method for word vectors that requires no pre-training data
  • Theory-Informed Model: Builds word representations from a theoretical model of language based on harmonic structure (Zipf’s Law)
  • Promising Performance: Demonstrates through preliminary experiments that, while not ideal out-of-the-box, the initialization can adapt effectively and approach the performance of pre-trained GloVe vectors when updated during downstream task training
  • Further Investigation: Sets the stage for future research into how contrastive information alone can produce competitive representations

Technical Innovation

EigenNoise creates word vector initializations by performing an eigen-decomposition on a theoretically-derived co-occurrence matrix. This matrix is constructed based on word rank-frequency, assuming all words co-occur at least once. This method provides a principled way to generate a starting point for word embeddings without observing any actual corpus data.

Significance

This work explores a core question: how effective is a representation learned from contrastive information alone? It offers a practical initialization strategy for resource-constrained scenarios and contributes to the broader understanding of what information is truly essential for building effective linguistic representations. The results invite further exploration of theory-informed, low-compute methods in NLP.

Citation

@article{heidenreich2022eigennoise,
  title={EigenNoise: A Contrastive Prior to Warm-Start Representations},
  author={Heidenreich, Hunter Scott and Williams, Jake Ryland},
  journal={arXiv preprint arXiv:2205.04376},
  year={2022}
}