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Learning sentence embeddings often requires a large amount of labeled data.However, for most tasks and domains, labeled data is seldom available andcreating it is expensive. In this work, we present a new state-of-the-artunsupervised method based on pre-trained Transformers and Sequential DenoisingAuto-Encoder (TSDAE) which outperforms previous approaches by up to 6.4 points.It can achieve up to 93.1% of the performance of in-domain supervisedapproaches. Further, we show that TSDAE is a strong domain adaptation andpre-training method for sentence embeddings, significantly outperforming otherapproaches like Masked Language Model.  A crucial shortcoming of previous studies is the narrow evaluation: Most workmainly evaluates on the single task of Semantic Textual Similarity (STS), whichdoes not require any domain knowledge. It is unclear if these proposed methodsgeneralize to other domains and tasks. We fill this gap and evaluate TSDAE andother recent approaches on four different datasets from heterogeneous domains.

TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning