@inproceedings{poerner-etal-2020-sentence,
    title = "Sentence Meta-Embeddings for Unsupervised Semantic Textual Similarity",
    author = {Poerner, Nina  and
      Waltinger, Ulli  and
      Sch{\"u}tze, Hinrich},
    editor = "Jurafsky, Dan  and
      Chai, Joyce  and
      Schluter, Natalie  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2020.acl-main.628/",
    doi = "10.18653/v1/2020.acl-main.628",
    pages = "7027--7034",
    abstract = {We address the task of unsupervised Semantic Textual Similarity (STS) by ensembling diverse pre-trained sentence encoders into sentence meta-embeddings. We apply, extend and evaluate different meta-embedding methods from the word embedding literature at the sentence level, including dimensionality reduction (Yin and Sch{\"u}tze, 2016), generalized Canonical Correlation Analysis (Rastogi et al., 2015) and cross-view auto-encoders (Bollegala and Bao, 2018). Our sentence meta-embeddings set a new unsupervised State of The Art (SoTA) on the STS Benchmark and on the STS12-STS16 datasets, with gains of between 3.7{\%} and 6.4{\%} Pearson{'}s r over single-source systems.}
}