Sentence Meta-Embeddings for Unsupervised Semantic Textual Similarity

Poerner, Nina; Waltinger, Ulli und Schütze, Hinrich (6. Juli 2020): Sentence Meta-Embeddings for Unsupervised Semantic Textual Similarity. , July 6 – 8, 2020, Seattle, USA

Abstract

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.

• Dokumententyp: Konferenz
• EU Funded Grant Agreement Number: 740516
• EU-Projekte: Horizon 2020 > ERC Grants > ERC Advanced Grant > ERC Grant 740516: NonSequeToR - Non-sequence models for tokenization replacement
• Fakultätsübergreifende Einrichtungen: Centrum für Informations- und Sprachverarbeitung (CIS)
• Themengebiete: 000 Informatik, Informationswissenschaft, allgemeine Werke > 000 Informatik, Wissen, Systeme ; 400 Sprache > 410 Linguistik
• Ort: Stroudsburg, USA
• Bemerkung: https://arxiv.org/abs/1911.03700
• Sprache: Englisch