  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 information provided by the multiple relations, rather than
treating them as separate tasks. Not only does this approach outperform the
single-task approach and the traditional multi-task learning approach, but it
also achieves state-of-the-art performance on all but one relation of the Human
Activity Phrase dataset.
