SCD: Self-Contrastive Decorrelation for Sentence Embeddings

Tassilo Klein; Moin Nabi

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arXiv · 2203.07847

SCD: Self-Contrastive Decorrelation for Sentence Embeddings

Tassilo Klein·Moin Nabi

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Abstract

In this paper, we propose Self-Contrastive Decorrelation (SCD), a self-supervised approach. Given an input sentence, it optimizes a joint self-contrastive and decorrelation objective. Learning a representation is facilitated by leveraging the contrast arising from the instantiation of standard dropout at different rates. The proposed method is conceptually simple yet empirically powerful. It achieves comparable results with state-of-the-art methods on multiple benchmarks without using contrastive pairs. This study opens up avenues for efficient self-supervised learning methods that are more robust than current contrastive methods.

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Tassilo Klein, Moin Nabi. 2022-03-15. SCD: Self-Contrastive Decorrelation for Sentence Embeddings. https://arxiv.org/abs/2203.07847

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