Detecting Attackable Sentences in Arguments
This work addresses the challenge of automated refutation in argumentation for online discourse, though it is incremental as it builds on existing analysis and machine learning approaches.
The paper tackled the problem of identifying attackable sentences in online arguments by analyzing driving reasons and sentence characteristics, and demonstrated that machine learning models can detect such sentences significantly better than baselines and comparably to laypeople.
Finding attackable sentences in an argument is the first step toward successful refutation in argumentation. We present a first large-scale analysis of sentence attackability in online arguments. We analyze driving reasons for attacks in argumentation and identify relevant characteristics of sentences. We demonstrate that a sentence's attackability is associated with many of these characteristics regarding the sentence's content, proposition types, and tone, and that an external knowledge source can provide useful information about attackability. Building on these findings, we demonstrate that machine learning models can automatically detect attackable sentences in arguments, significantly better than several baselines and comparably well to laypeople.