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Vector representations of sentences, trained on massive text corpora, are widely used as generic sentence embeddings across a variety of NLP problems. The learned representations are generally assumed to be continuous and real-valued, giving rise to a large memory footprint and slow retrieval speed, which hinders their applicability to low-resource (memory and computation) platforms, such as mobile devices. In this paper, we propose four different strategies to transform continuous and genericdoi:10.18653/v1/p19-1011 dblp:conf/acl/ShenCSZYTCC19 fatcat:34mqfel4snfrtfqpsf4qi43e7e