@inproceedings{v-ganesan-etal-2021-empirical,
title = "Empirical Evaluation of Pre-trained Transformers for Human-Level {NLP}: The Role of Sample Size and Dimensionality",
author = "V Ganesan, Adithya and
Matero, Matthew and
Ravula, Aravind Reddy and
Vu, Huy and
Schwartz, H. Andrew",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.357",
doi = "10.18653/v1/2021.naacl-main.357",
pages = "4515--4532",
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 1/12 of the embedding dimensions.",
}
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%0 Conference Proceedings
%T Empirical Evaluation of Pre-trained Transformers for Human-Level NLP: The Role of Sample Size and Dimensionality
%A V Ganesan, Adithya
%A Matero, Matthew
%A Ravula, Aravind Reddy
%A Vu, Huy
%A Schwartz, H. Andrew
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2021
%8 jun
%I Association for Computational Linguistics
%C Online
%F v-ganesan-etal-2021-empirical
%X 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 1/12 of the embedding dimensions.
%R 10.18653/v1/2021.naacl-main.357
%U https://aclanthology.org/2021.naacl-main.357
%U https://doi.org/10.18653/v1/2021.naacl-main.357
%P 4515-4532
Markdown (Informal)
[Empirical Evaluation of Pre-trained Transformers for Human-Level NLP: The Role of Sample Size and Dimensionality](https://aclanthology.org/2021.naacl-main.357) (V Ganesan et al., NAACL 2021)
ACL