  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.
