  We introspect black-box sentence embeddings by conditionally generating from
them with the objective to retrieve the underlying discrete sentence. We
perceive of this as a new unsupervised probing task and show that it correlates
well with downstream task performance. We also illustrate how the language
generated from different encoders differs. We apply our approach to generate
sentence analogies from sentence embeddings.
