# Contextualizing Argument Quality Assessment with Relevant Knowledge

arXiv: https://arxiv.org/abs/2305.12280 · HTML: https://mlsanity.com/paper/2305.12280/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 47 references that are in the map.
- First posted on arXiv 20 May 2023.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [A Large-scale Dataset for Argument Quality Ranking: Construction and Analysis](https://mlsanity.com/paper/1911.11408/) (2019)
- [Automatic Argument Quality Assessment -- New Datasets and Methods](https://mlsanity.com/paper/1909.01007/) (2019)
- [End-to-End Retrieval in Continuous Space](https://mlsanity.com/paper/1811.08008/) (2018)
- [AMPERSAND: Argument Mining for PERSuAsive oNline Discussions](https://mlsanity.com/paper/2004.14677/) (2020)
- [Exploring the Role of Prior Beliefs for Argument Persuasion](https://mlsanity.com/paper/1906.11301/) (2019)
- [Rhetoric, Logic, and Dialectic: Advancing Theory-based Argument Quality Assessment in Natural Language Processing](https://mlsanity.com/paper/2006.00843/) (2020)
- [Scientia Potentia Est -- On the Role of Knowledge in Computational Argumentation](https://mlsanity.com/paper/2107.00281/) (2021)
- [Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale](https://mlsanity.com/paper/2101.10250/) (2021)
- [Automated Essay Scoring Using Transformer Models](https://mlsanity.com/paper/2110.06874/) (2021)
- [Assessing the Sufficiency of Arguments through Conclusion Generation](https://mlsanity.com/paper/2110.13495/) (2021)
- [Towards a Holistic View on Argument Quality Prediction](https://mlsanity.com/paper/2205.09803/) (2022)

## Built on by (most cited first, 4 of 4)

- [TRAIL: Trace Reasoning and Agentic Issue Localization](https://mlsanity.com/paper/2505.08638/) (2025)
- [Leveraging Small LLMs for Argument Mining in Education: Argument Component Identification, Classification, and Assessment](https://mlsanity.com/paper/2502.14389/) (2025)
- [ArgBench: Benchmarking LLMs on Computational Argumentation Tasks](https://mlsanity.com/paper/2604.17366/) (2026)
- [Argument Quality Assessment with Large Language Models: A Pairwise Bradley-Terry Approach](https://mlsanity.com/paper/2605.28313/) (2026)

## Abstract (arXiv)

Automatic assessment of the quality of arguments has been recognized as a challenging task with significant implications for misinformation and targeted speech. While real-world arguments are tightly anchored in context, existing computational methods analyze their quality in isolation, which affects their accuracy and generalizability. We propose SPARK: a novel method for scoring argument quality based on contextualization via relevant knowledge. We devise four augmentations that leverage large language models to provide feedback, infer hidden assumptions, supply a similar-quality argument, or give a counter-argument. SPARK uses a dual-encoder Transformer architecture to enable the original argument and its augmentation to be considered jointly. Our experiments in both in-domain and zero-shot setups show that SPARK consistently outperforms existing techniques across multiple metrics.