A Comprehensive Survey of Sentence Representations: From the BERT Epoch to the ChatGPT Era and Beyond
This survey addresses the lack of literature reviews in sentence representations for NLP researchers and practitioners, but it is incremental as it synthesizes existing work without introducing new methods.
The paper provides a comprehensive survey of sentence representation learning methods, focusing on deep learning models from the BERT era onward, and organizes the literature to highlight key contributions and challenges.
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.