@inproceedings{xiaoming-etal-2023-sentbench,
    title = "{S}ent{B}ench: Comprehensive Evaluation of Self-Supervised Sentence Representation with Benchmark Construction",
    author = "Xiaoming, Liu  and
      Hongyu, Lin  and
      Xianpei, Han  and
      Le, Sun",
    editor = "Sun, Maosong  and
      Qin, Bing  and
      Qiu, Xipeng  and
      Jiang, Jing  and
      Han, Xianpei",
    booktitle = "Proceedings of the 22nd Chinese National Conference on Computational Linguistics",
    month = aug,
    year = "2023",
    address = "Harbin, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2023.ccl-1.69/",
    pages = "813--823",
    language = "eng",
    abstract = "{\textquotedblleft}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.{\textquotedblright}"
}