@inproceedings{zhang-etal-2024-evaluating-unsupervised,
    title = "Evaluating Unsupervised Dimensionality Reduction Methods for Pretrained Sentence Embeddings",
    author = "Zhang, Gaifan  and
      Zhou, Yi  and
      Bollegala, Danushka",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.579",
    pages = "6530--6543",
    abstract = "Sentence embeddings produced by Pretrained Language Models (PLMs) have received wide attention from the NLP community due to their superior performance when representing texts in numerous downstream applications. However, the high dimensionality of the sentence embeddings produced by PLMs is problematic when representing large numbers of sentences in memory- or compute-constrained devices. As a solution, we evaluate unsupervised dimensionality reduction methods to reduce the dimensionality of sentence embeddings produced by PLMs. Our experimental results show that simple methods such as Principal Component Analysis (PCA) can reduce the dimensionality of sentence embeddings by almost 50{\%}, without incurring a significant loss in performance in multiple downstream tasks. Surprisingly, reducing the dimensionality further \textit{improves} performance over the original high dimensional versions for the sentence embeddings produced by some PLMs in some tasks.",
}

Sentence embeddings produced by Pretrained Language Models (PLMs) have received wide attention from the NLP community due to their superior performance when representing texts in numerous downstream applications. However, the high dimensionality of the sentence embeddings produced by PLMs is problematic when representing large numbers of sentences in memory- or compute-constrained devices. As a solution, we evaluate unsupervised dimensionality reduction methods to reduce the dimensionality of sentence embeddings produced by PLMs. Our experimental results show that simple methods such as Principal Component Analysis (PCA) can reduce the dimensionality of sentence embeddings by almost 50{\%}, without incurring a significant loss in performance in multiple downstream tasks. Surprisingly, reducing the dimensionality further \textit{improves} performance over the original high dimensional versions for the sentence embeddings produced by some PLMs in some tasks.
Zhang, Gaifan Zhou, Yi Bollegala, Danushka
Evaluating Unsupervised Dimensionality Reduction Methods for Pretrained Sentence Embeddings