Argument Mining using BERT and Self-Attention based Embeddings
This work addresses the challenge of extracting complex argument structures in online forums, which is an incremental advancement in the field of argument mining.
The paper tackles the problem of argument mining by proposing a novel methodology that uses attention-based embeddings for link prediction to model complex argument structures in online discourse, achieving improved performance over traditional tree-like and linguistic modeling approaches.
Argument mining automatically identifies and extracts the structure of inference and reasoning conveyed in natural language arguments. To the best of our knowledge, most of the state-of-the-art works in this field have focused on using tree-like structures and linguistic modeling. But, these approaches are not able to model more complex structures which are often found in online forums and real world argumentation structures. In this paper, a novel methodology for argument mining is proposed which employs attention-based embeddings for link prediction to model the causational hierarchies in typical argument structures prevalent in online discourse.