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Multi-relational semantic similarity datasets define the semantic relations between two short texts in multiple ways, e.g., similarity, relatedness, and so on. Yet, all the systems to date designed to capture such relations target one relation at a time. We propose a multi-label transfer learning approach based on LSTM to make predictions for several relations simultaneously and aggregate the losses to update the parameters. This multi-label regression approach jointly learns the informationdoi:10.18653/v1/s19-1005 dblp:conf/starsem/ZhangWM19 fatcat:ozugcxpmmrft3hhfjsntsoqy74