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Neural End-to-End Learning for Computational Argumentation Mining

Abstract · Apr 20, 2017 12:20 ·

argumentation subtasks argument parsing stab end gurevych cs-cl

Arxiv Abstract

  • Steffen Eger
  • Johannes Daxenberger
  • Iryna Gurevych

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. Moreover, we find that jointly learning ‘natural’ subtasks, in a multi-task learning setup, improves performance.

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