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Found 4 papers, 3 papers with code
Data Augmentation for Improving the Prediction of Validity and Novelty of Argumentative Conclusions
Using only training data retrieved from related datasets by automatically labeling them for validity and novelty, combined with synthetic data, outperforms the baseline by 11. 5 points in F_1-score.
Overview of the 2022 Validity and Novelty Prediction Shared Task
This paper provides an overview of the Argument Validity and Novelty Prediction Shared Task that was organized as part of the 9th Workshop on Argument Mining (ArgMining 2022).
Ranked #1 on
ValNov
on ValNov Subtask A
Graph Language Models
In our work we introduce a novel LM type, the Graph Language Model (GLM), that integrates the strengths of both approaches and mitigates their weaknesses.
Similarity-weighted Construction of Contextualized Commonsense Knowledge Graphs for Knowledge-intense Argumentation Tasks
Arguments often do not make explicit how a conclusion follows from its premises.
