IUBC at SemEval-2016 task 2: Rnns and lstms for interpretable STS

Abstract

This paper describes iUBC, a neural network based approach that achieves competitive results on the interpretable STS task (iSTS 2016). Actually, it achieves top performance in one of the three datasets. iUBC makes use of a jointly trained classifier and regressor, and both models work on top of a recurrent neural network. Through the paper we provide detailed description of the approach, as well as the results obtained in iSTS 2015 test, iSTS 2016 training and iSTS 2016 test.