Sentence representations have become a critical component in natural language processing applications, such as retrieval, question answering, and text classification. They capture the semantics and 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. In this paper, we provide an overview of the different methods for sentence representation learning, including both traditional and deep learning-based techniques. We provide a systematic organization of the literature on sentence representation learning, highlighting the key contributions and challenges in this area. Overall, our review highlights the progress made in sentence representation learning, the importance of this area in natural language processing, and the challenges that remain. We conclude with directions for future research, suggesting potential avenues for improving the quality and efficiency of sentence representations in NLP applications.

翻译：句子表示已成为自然语言处理应用（如检索、问答和文本分类）中的关键组成部分。它们捕捉句子的语义和含义，使机器能够理解并推理人类语言。近年来，在开发句子表示学习方法方面取得了显著进展，包括无监督、有监督和迁移学习等方法。本文概述了不同的句子表示学习方法，涵盖传统方法和基于深度学习的技术。我们对句子表示学习的相关文献进行了系统梳理，突出了该领域的主要贡献和挑战。总体而言，我们的综述强调了句子表示学习取得的进展、该领域在自然语言处理中的重要性以及尚存的挑战。最后，我们提出了未来研究方向，为提升自然语言处理应用中句子表示的质量与效率提供潜在途径。