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.

翻译：句子表示是检索、问答和文本分类等自然语言处理应用中的关键组成部分。它捕捉句子的语义，使机器能够理解和推理人类语言。近年来，在句子表示学习方法（包括无监督、有监督和迁移学习方法）的开发方面取得了显著进展。然而，迄今为止尚缺乏对句子表示的文献综述。本文概述了不同的句子表示学习方法，主要关注深度学习模型。我们对相关文献进行了系统化整理，重点阐述了该领域的关键贡献与挑战。总体而言，本综述强调了句子表示在自然语言处理中的重要性、学习方法的进展以及存在的挑战。最后，我们指出了未来研究方向，提出了改进句子表示质量与效率的可能途径。