Argumentative Relation Classification as Plausibility Ranking

By Juri Opitz. 2019

15 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Information Systems, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Information Systems

In Natural Language Processing

Citations in the corpus

Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference (2020, neutral): “, 2019) and argumentative relation classification (Opitz, 2019).”
It’s Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners (2020, methodology): “gumentative relation classification (Opitz, 2019).”
Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing (2021, methodology): “For example, Opitz [93]’s argRanker method conceptualizes argumentative relation classification as a ranking problem for two sentences that are connected by manually defined phrases.”
Generating Datasets with Pretrained Language Models (2021, neutral): “PLMs with task descriptions for zero- or few-shot learning has been studied extensively (e.g., Radford et al., 2019; Puri and Catanzaro, 2019; Opitz, 2019; Brown et al., 2020; Schick and Schütze, 2020a,b, 2021; Weller et al., 2020; Gao et al., 2020; Tam et al., 2021).”
Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification (2020, background): “, 2019), in few-shot scenarios with limited amounts of training data (Opitz, 2019; Shwartz et al., 2020; Brown et al., 2020), and even in high-resource settings (Raffel et al.”
True Few-Shot Learning with Prompts—A Real-World Perspective (2021, methodology): “This idea has since been applied to solve a wide range of NLP tasks without any task-specific training data (Puri and Catanzaro, 2019; Opitz, 2019; Davison et al., 2019; Schick et al., 2021; Schick and Sch¨utze, 2021; Wei et al., 2022; Sanh et al., 2022).”
A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios (2020, background): “A pretrained language model then judges the label sentence that most likely follows the unlabeled input (Opitz, 2019; Schick and Schütze, 2020; Schick et al., 2020).”