  Sentence embedding refers to a set of effective and versatile techniques for
converting raw text into numerical vector representations that can be used in a
wide range of natural language processing (NLP) applications. The majority of
these techniques are either supervised or unsupervised. Compared to the
unsupervised methods, the supervised ones make less assumptions about
optimization objectives and usually achieve better results. However, the
training requires a large amount of labeled sentence pairs, which is not
available in many industrial scenarios. To that end, we propose a generic and
end-to-end approach -- PAUSE (Positive and Annealed Unlabeled Sentence
Embedding), capable of learning high-quality sentence embeddings from a
partially labeled dataset. We experimentally show that PAUSE achieves, and
sometimes surpasses, state-of-the-art results using only a small fraction of
labeled sentence pairs on various benchmark tasks. When applied to a real
industrial use case where labeled samples are scarce, PAUSE encourages us to
extend our dataset without the liability of extensive manual annotation work.
