A New Direction in Stance Detection: Target-Stance Extraction in the Wild

Stance detection aims to detect the stance toward a corresponding target. Existing works have achieved promising progress on stance detection tasks in which the goal is to predict the stance given both a target and a text. However, they all work under the assumption that the target is known in advance, which is often not the case in the wild. Given a text from social media platforms, the target information is often unknown due to implicit mentions in the source text and it is infeasible to have manual target annotations at a large scale. Therefore, in this paper, we propose a new task Target-Stance Extraction (TSE) that aims to extract the (target, stance) pair from the text. We benchmark the task by proposing a two-stage framework that first identifies the relevant target in the text and then detects the stance given the predicted target and text. Specifically, we first propose two different settings: Target Classification and Target Generation, to identify the potential target from a given text. Then we propose a multitask approach that takes target prediction as the auxiliary task to detect the stance toward the predicted target. We evaluate the proposed framework on both in-target stance detection in which the test target is always seen in the training stage and zero-shot stance detection that needs to detect the stance for the unseen target during the inference stage. The new TSE task can facilitate future research in the field of stance detection. We publicly release our code. 1