# Riposte! A Large Corpus of Counter-Arguments

arXiv: https://arxiv.org/abs/1910.03246 · HTML: https://mlsanity.com/paper/1910.03246/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2019.

## Built on by (most cited first, 2 of 2)

- [MAFALDA: A Benchmark and Comprehensive Study of Fallacy Detection and Classification](https://mlsanity.com/paper/2311.09761/) (2023)
- [LPAttack: A Feasible Annotation Scheme for Capturing Logic Pattern of Attacks in Arguments](https://mlsanity.com/paper/2204.01512/) (2022)

## Abstract (arXiv)

Constructive feedback is an effective method for improving critical thinking skills. Counter-arguments (CAs), one form of constructive feedback, have been proven to be useful for critical thinking skills. However, little work has been done for constructing a large-scale corpus of them which can drive research on automatic generation of CAs for fallacious micro-level arguments (i.e. a single claim and premise pair). In this work, we cast providing constructive feedback as a natural language processing task and create Riposte!, a corpus of CAs, towards this goal. Produced by crowdworkers, Riposte! contains over 18k CAs. We instruct workers to first identify common fallacy types and produce a CA which identifies the fallacy. We analyze how workers create CAs and construct a baseline model based on our analysis.