KNOWCOMP POKEMON Team at DialAM-2024: A Two-Stage Pipeline for Detecting Relations in Dialogical Argument Mining
This work addresses the problem of detecting argumentative and illocutionary relations in dialogues for the argument mining community, representing an incremental improvement in a specific shared task.
The paper tackled the task of dialogical argument mining by proposing a two-stage pipeline with data augmentation and context introduction, achieving first place in the ARI Focused score and fourth in the Global Focused score in the DialAM-2024 shared task.
Dialogical Argument Mining(DialAM) is an important branch of Argument Mining(AM). DialAM-2024 is a shared task focusing on dialogical argument mining, which requires us to identify argumentative relations and illocutionary relations among proposition nodes and locution nodes. To accomplish this, we propose a two-stage pipeline, which includes the Two-Step S-Node Prediction Model in Stage 1 and the YA-Node Prediction Model in Stage 2. We also augment the training data in both stages and introduce context in Stage 2. We successfully completed the task and achieved good results. Our team Pokemon ranked 1st in the ARI Focused score and 4th in the Global Focused score.