@inproceedings{ramesh-kashyap-etal-2024-comprehensive,
title = "A Comprehensive Survey of Sentence Representations: From the {BERT} Epoch to the {CHATGPT} Era and Beyond",
author = "Ramesh Kashyap, Abhinav and
Nguyen, Thanh-Tung and
Schlegel, Viktor and
Winkler, Stefan and
Ng, See-Kiong and
Poria, Soujanya",
editor = "Graham, Yvette and
Purver, Matthew",
booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = mar,
year = "2024",
address = "St. Julian{'}s, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.eacl-long.104",
pages = "1738--1751",
abstract = "Sentence representations are a critical component in NLP applications such as retrieval, question answering, and text classification. They capture the meaning of a sentence, enabling machines to understand and reason over human language. In recent years, significant progress has been made in developing methods for learning sentence representations, including unsupervised, supervised, and transfer learning approaches. However there is no literature review on sentence representations till now. In this paper, we provide an overview of the different methods for sentence representation learning, focusing mostly on deep learning models. We provide a systematic organization of the literature, highlighting the key contributions and challenges in this area. Overall, our review highlights the importance of this area in natural language processing, the progress made in sentence representation learning, and the challenges that remain. We conclude with directions for future research, suggesting potential avenues for improving the quality and efficiency of sentence representations.",
}
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<abstract>Sentence representations are a critical component in NLP applications such as retrieval, question answering, and text classification. They capture the meaning of a sentence, enabling machines to understand and reason over human language. In recent years, significant progress has been made in developing methods for learning sentence representations, including unsupervised, supervised, and transfer learning approaches. However there is no literature review on sentence representations till now. In this paper, we provide an overview of the different methods for sentence representation learning, focusing mostly on deep learning models. We provide a systematic organization of the literature, highlighting the key contributions and challenges in this area. Overall, our review highlights the importance of this area in natural language processing, the progress made in sentence representation learning, and the challenges that remain. We conclude with directions for future research, suggesting potential avenues for improving the quality and efficiency of sentence representations.</abstract>
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%0 Conference Proceedings
%T A Comprehensive Survey of Sentence Representations: From the BERT Epoch to the CHATGPT Era and Beyond
%A Ramesh Kashyap, Abhinav
%A Nguyen, Thanh-Tung
%A Schlegel, Viktor
%A Winkler, Stefan
%A Ng, See-Kiong
%A Poria, Soujanya
%Y Graham, Yvette
%Y Purver, Matthew
%S Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F ramesh-kashyap-etal-2024-comprehensive
%X Sentence representations are a critical component in NLP applications such as retrieval, question answering, and text classification. They capture the meaning of a sentence, enabling machines to understand and reason over human language. In recent years, significant progress has been made in developing methods for learning sentence representations, including unsupervised, supervised, and transfer learning approaches. However there is no literature review on sentence representations till now. In this paper, we provide an overview of the different methods for sentence representation learning, focusing mostly on deep learning models. We provide a systematic organization of the literature, highlighting the key contributions and challenges in this area. Overall, our review highlights the importance of this area in natural language processing, the progress made in sentence representation learning, and the challenges that remain. We conclude with directions for future research, suggesting potential avenues for improving the quality and efficiency of sentence representations.
%U https://aclanthology.org/2024.eacl-long.104
%P 1738-1751
Markdown (Informal)
[A Comprehensive Survey of Sentence Representations: From the BERT Epoch to the CHATGPT Era and Beyond](https://aclanthology.org/2024.eacl-long.104) (Ramesh Kashyap et al., EACL 2024)
ACL