TriTSP: A triangular joint reasoning networks for target–stance prediction

Target–stance prediction is a novel task evolved from the traditional stance detection task, aiming to predict the pair of target and stance from each tweet. The target–stance prediction task is currently solved by the two-stage method. Although this method effectively alleviates the dependence on manually labeled target information, the errors generated in the first-stage target identification task will directly have a negative impact on the performance of the second-stage stance detection task, resulting in obvious error cascades. Moreover, it is difficult to establish effective feature interactions between the two subtasks. To tackle the above problems, we propose a triangular joint reasoning model named TriTSP. The proposed model unifies the target features and stance features in the joint prediction manner to capture the correlations and interactions between them. Furthermore, inspired by the way humans express stances, we incorporate expanded stance triangle framework into our model to infer the specified target–stance pair through the explicit pairs contained in social media. Our proposed model not only eliminates error cascades, but also effectively improves the performance of the target–stance prediction task. Experiments on two benchmark datasets demonstrate that our proposed model has significant advantages over the current state-of-the-art models.