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Poerner, Nina; Waltinger, Ulli and Schütze, Hinrich (6. July 2020): Sentence Meta-Embeddings for Unsupervised Semantic Textual Similarity. UNSPECIFIED, July 6 – 8, 2020, Seattle, USA [PDF, 386kB]


We address the task of unsupervised Seman- tic Textual Similarity (STS) by ensembling di- verse pre-trained sentence encoders into sen- tence meta-embeddings. We apply, extend and evaluate different meta-embedding meth- ods from the word embedding literature at the sentence level, including dimensionality re- duction (Yin and Schu ̈tze, 2016), generalized Canonical Correlation Analysis (Rastogi et al., 2015) and cross-view auto-encoders (Bolle- gala 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 be- tween 3.7% and 6.4% Pearson’s r over single- source systems.

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