Sentence Meta-Embeddings for Unsupervised Semantic Textual Similarity

Nina Poerner; Ulli Waltinger; Hinrich Schütze

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arXiv · 1911.03700

Sentence Meta-Embeddings for Unsupervised Semantic Textual Similarity

Nina Poerner·Ulli Waltinger·Hinrich Schütze

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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\"utze, 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.

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Nina Poerner, Ulli Waltinger, Hinrich Schütze. 2019-11-09. Sentence Meta-Embeddings for Unsupervised Semantic Textual Similarity. https://arxiv.org/abs/1911.03700

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