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Understanding Learning Dynamics Of Language Models with SVCCA

Paper · 2019 · Naomi Saphra and Adam Lopez

Summary

Earlier work showed that neural networks implicitly capture linguistic categories, but not how those encodings develop during training. Saphra and Lopez examine this for language models. They train a two-layer LSTM LM on lowercased English Wikipedia and save it after every epoch. At each snapshot, they compare its embedding and recurrent layers with the internal states of separately trained LSTM taggers. The taggers cover four kinds of labels: silver UDP POS, PTB POS from the Groningen Meaning Bank, SEM tags, and the topic of the source Wikipedia article. The comparison uses SVCCA, which first trims noisy dimensions with SVD and then measures how well the two representations correlate in a shared subspace found by CCA. This avoids training a new probe for every layer and epoch.

The LM acquires POS-like structure early in training, while topic information keeps developing later. Over time, recurrent layers grow more similar to the taggers, and embedding layers grow less similar. These findings could guide learning algorithms that make better use of linguistic structure.

Key points

  • LM training data: more than 227 million tokens with a 20K-type vocabulary, from English Wikipedia (70/10/20 split). Training ran for 50 epochs with SGD, and the learning rate was quartered whenever validation loss rose.
  • SVCCA settings: the SVD step keeps enough dimensions to cover 99% of the variance. The empirical ceiling, measured against an identically built LM with a different initialization, peaks around 0.64.
  • Tag sets: UDP POS (spacy) and topic labels come from the 100 longest Wikipedia articles. SEM tags have 66 fine classes grouped into 13 coarse ones. Representations are compared on a shared 191K-token test corpus.
  • Convergence order: in this shallow model, the first recurrent layer settles slightly after the second. This contrasts with Raghu et al. (2017), and the authors attribute the difference to their much larger training data.
  • Efficiency and future work: the SVCCA experiments finished in hours, whereas diagnostic-classifier probing took days. The authors suggest future work on gradient starvation and on analysing ELMo.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 9,086 worksArtificial Intelligence · 8,889 worksArts and Humanities · 9,296 worksLanguage and Linguistics · 9,206 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 1,343 works

Cite

Cite as Naomi Saphra and Adam Lopez. 2019. Understanding Learning Dynamics Of Language Models with SVCCA. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/n19-1329

@inproceedings{saphra2019understanding,
  title = {{Understanding Learning Dynamics Of Language Models with SVCCA}},
  author = {Naomi Saphra and Adam Lopez},
  booktitle = {Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)},
  year = {2019},
  doi = {10.18653/v1/n19-1329},
  url = {https://doi.org/10.18653/v1/n19-1329}
}

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