Martin Kerscher


2020

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Vec2Sent: Probing Sentence Embeddings with Natural Language Generation
Martin Kerscher | Steffen Eger
Proceedings of the 28th International Conference on Computational Linguistics

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.
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