Neural End-to-End Learning for Computational Argumentation Mining

Understand

We investigate neural techniques for end-to-end computational argumentation mining (AM).

• We frame AM both as a token-based dependency parsing and as a token-based sequence tagging problem, including a multi-task learning setup.
• Contrary to models that operate on the argument component level, we find that framing AM as dependency parsing leads to subpar performance results.
• In contrast, less complex (local) tagging models based on BiLSTMs perform robustly across classification scenarios, being able to catch long-range dependencies inherent to the AM problem.