Empirical Evaluation of Pre-trained Transformers for Human-Level NLP: The Role of Sample Size and Dimensionality

• URL: http://arxiv.org/abs/2105.03484v1
• Date: Fri, 7 May 2021 20:06:24 GMT
• Title: Empirical Evaluation of Pre-trained Transformers for Human-Level NLP: The Role of Sample Size and Dimensionality
• Authors: Adithya V Ganesan, Matthew Matero, Aravind Reddy Ravula, Huy Vu and H. Andrew Schwartz
• Abstract summary: RoBERTa consistently achieves top performance in human-level tasks, with PCA giving benefit over other reduction methods in better handling users that write longer texts. A majority of the tasks achieve results comparable to the best performance with just $frac112$ of the embedding dimensions.
• Score: 6.540382797747107
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In human-level NLP tasks, such as predicting mental health, personality, or demographics, the number of observations is often smaller than the standard 768+ hidden state sizes of each layer within modern transformer-based language models, limiting the ability to effectively leverage transformers. Here, we provide a systematic study on the role of dimension reduction methods (principal components analysis, factorization techniques, or multi-layer auto-encoders) as well as the dimensionality of embedding vectors and sample sizes as a function of predictive performance. We first find that fine-tuning large models with a limited amount of data pose a significant difficulty which can be overcome with a pre-trained dimension reduction regime. RoBERTa consistently achieves top performance in human-level tasks, with PCA giving benefit over other reduction methods in better handling users that write longer texts. Finally, we observe that a majority of the tasks achieve results comparable to the best performance with just $\frac{1}{12}$ of the embedding dimensions.

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