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In this work, we evaluate different sentence encoders with emphasis on examining their embedding spaces. Specifically, we hypothesize that a "high-quality" embedding aids in generalization, promoting transfer learning as well as zero-shot and one-shot learning. To investigate this, we modify Skipthought vectors to learn a more generalizable space by exploiting a small amount of supervision. The aim is to introduce an additional notion of similarity in the embeddings, rendering the vectorsdoi:10.18653/v1/w16-1628 dblp:conf/rep4nlp/TriantafillouKU16 fatcat:bpoek7umovhn3hn5g6w3soae7a