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Context plays an important role in human language understanding, thus it may also be useful for machines learning vector representations of language. In this paper, we explore an asymmetric encoder-decoder structure for unsupervised context-based sentence representation learning. We carefully designed experiments to show that neither an autoregressive decoder nor an RNN decoder is required. After that, we designed a model which still keeps an RNN as the encoder, while using a non-autoregressivedoi:10.18653/v1/w18-3009 dblp:conf/rep4nlp/TangJFWS18 fatcat:azhyp36ptnd7lh7jnzrpn3wtqm