“Self-supervised learning has been widely used to learn effective sentence representations. Previ-ous evaluation of sentence representations mainly focuses on the limited combination of tasks andparadigms while failing to evaluate their effectiveness in a wider range of application scenarios. Such divergences prevent us from understanding the limitations of current sentence representa-tions, as well as the connections between learning approaches and downstream applications. Inthis paper, we propose SentBench, a new comprehensive benchmark to evaluate sentence repre-sentations. SentBench covers 12 kinds of tasks and evaluates sentence representations with threetypes of different downstream application paradigms. Based on SentBench, we re-evaluate sev-eral frequently used self-supervised sentence representation learning approaches. Experimentsshow that SentBench can effectively evaluate sentence representations from multiple perspec-tives, and the performance on SentBench leads to some novel findings which enlighten futureresearches.”