A Hierarchical Sequence Labeling Model for Argument Pair Extraction.

Argument pair extraction (APE) is a new task in the field of argument mining, aiming at mining interactive argument pairs from peer review and rebuttal. Previous work framed APE as a combination of a sequence labeling task and a sentence pair classification task via multi-task learning. However, this method lacks explicit modeling of the relations between arguments, and the two subtasks may not cooperate well. Towards these issues, we propose a hierarchical sequence labeling model that can efficiently represent arguments and explicitly capture the correlations between them. Our method matches argument pairs from the perspective of review and rebuttal respectively, and merges the results, enabling a more comprehensive extraction of argument pairs. Also, we propose a series of improved models and fuse them using weighted voting. Our method achieved first place in the NLPCC 2021 Shared Task on APE, proving the effectiveness of our method.