# Argumentative Relation Classification as Plausibility Ranking

arXiv: https://arxiv.org/abs/1909.09031 · HTML: https://mlsanity.com/paper/1909.09031/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2019.

## Built on by (most cited first, 6 of 6)

- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Generating Datasets with Pretrained Language Models](https://mlsanity.com/paper/2104.07540/) (2021)
- [Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models](https://mlsanity.com/paper/2106.13353/) (2021)
- [True Few-Shot Learning with Prompts -- A Real-World Perspective](https://mlsanity.com/paper/2111.13440/) (2021)
- [Multi-Task Attentive Residual Networks for Argument Mining](https://mlsanity.com/paper/2102.12227/) (2021)
- [Cross-Genre Argument Mining: Can Language Models Automatically Fill in Missing Discourse Markers?](https://mlsanity.com/paper/2306.04314/) (2023)

## Abstract (arXiv)

We formulate argumentative relation classification (support vs. attack) as a text-plausibility ranking task. To this aim, we propose a simple reconstruction trick which enables us to build minimal pairs of plausible and implausible texts by simulating natural contexts in which two argumentative units are likely or unlikely to appear. We show that this method is competitive with previous work albeit it is considerably simpler. In a recently introduced content-based version of the task, where contextual discourse clues are hidden, the approach offers a performance increase of more than 10% macro F1. With respect to the scarce attack-class, the method achieves a large increase in precision while the incurred loss in recall is small or even nonexistent.