Beyond Words: A Comprehensive Survey of Sentence Representations

Anonymous

Abstract: Sentence representations are a critical component in several 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. 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 on sentence representation learning, 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.

Paper Type: long

Research Area: Information Retrieval and Text Mining

Contribution Types: Surveys

Languages Studied: English