Motivated by recent advances in multi-task learning (Liu et al., , 2017Balikas et al., 2017;Yu et al., 2018;Cohan et al., 2019) and the effectiveness of sentiment information in stance detection (Sobhani et al., 2016;Sun et al., 2018Sun et al., , 2019, we propose an attention-based multi-task framework that takes sentiment classification as an auxiliary task for stance detection.
we first encode words of input sentences in fixed-length dense vectors (Mikolov et al., 2013;Bojanowski et al., 2017) and then feed them as input to Bidirectional Long Short-Term Memory networks (BiLSTM) (Hochreiter and Schmidhuber, 1997;Schuster and Paliwal, 1997).