Distant finetuning with discourse relations for stance classification
This addresses stance classification for debates and fake news detection, offering an incremental improvement through automated data extraction and noise reduction.
The authors tackled stance classification by proposing a topic-independent method that extracts silver-labeled data using discourse relations and a 3-stage training framework with decreasing noise levels, achieving first place among 26 teams in the NLPCC 2021 shared task.
Approaches for the stance classification task, an important task for understanding argumentation in debates and detecting fake news, have been relying on models which deal with individual debate topics. In this paper, in order to train a system independent from topics, we propose a new method to extract data with silver labels from raw text to finetune a model for stance classification. The extraction relies on specific discourse relation information, which is shown as a reliable and accurate source for providing stance information. We also propose a 3-stage training framework where the noisy level in the data used for finetuning decreases over different stages going from the most noisy to the least noisy. Detailed experiments show that the automatically annotated dataset as well as the 3-stage training help improve model performance in stance classification. Our approach ranks 1st among 26 competing teams in the stance classification track of the NLPCC 2021 shared task Argumentative Text Understanding for AI Debater, which confirms the effectiveness of our approach.