# Automatic Debate Evaluation with Argumentation Semantics and Natural Language Argument Graph Networks

arXiv: https://arxiv.org/abs/2203.14647 · HTML: https://mlsanity.com/paper/2203.14647/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 43 references that are in the map.
- First posted on arXiv 28 Mar 2022.

## Builds on (most cited first, 12 of 12)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://mlsanity.com/paper/2004.09813/) (2020)
- [From Arguments to Key Points: Towards Automatic Argument Summarization](https://mlsanity.com/paper/2005.01619/) (2020)
- [DebateSum: A large-scale argument mining and summarization dataset](https://mlsanity.com/paper/2011.07251/) (2020)
- [Are You Convinced? Choosing the More Convincing Evidence with a Siamese Network](https://mlsanity.com/paper/1907.08971/) (2019)
- [A Corpus for Modeling User and Language Effects in Argumentation on Online Debating](https://mlsanity.com/paper/1906.11310/) (2019)
- [Transformer-Based Models for Automatic Identification of Argument Relations: A Cross-Domain Evaluation](https://mlsanity.com/paper/2011.13187/) (2020)
- [A Dataset of General-Purpose Rebuttal](https://mlsanity.com/paper/1909.00393/) (2019)
- [Towards an Argument Mining Pipeline Transforming Texts to Argument Graphs](https://mlsanity.com/paper/2006.04562/) (2020)
- [Deep Learning for Abstract Argumentation Semantics](https://mlsanity.com/paper/2007.07629/) (2020)

## Built on by (most cited first, 11 of 11)

- [Evaluating LLM-based Agents for Multi-Turn Conversations: A Survey](https://mlsanity.com/paper/2503.22458/) (2025)
- [Debatrix: Multi-dimensional Debate Judge with Iterative Chronological Analysis Based on LLM](https://mlsanity.com/paper/2403.08010/) (2024)
- [An Empirical Analysis on Large Language Models in Debate Evaluation](https://mlsanity.com/paper/2406.00050/) (2024)
- [Large Language Models in Argument Mining: A Survey](https://mlsanity.com/paper/2506.16383/) (2025)
- [A Methodology for Incompleteness-Tolerant and Modular Gradual Semantics for Argumentative Statement Graphs](https://mlsanity.com/paper/2410.22209/) (2024)
- [DebateKG: Automatic Policy Debate Case Creation with Semantic Knowledge Graphs](https://mlsanity.com/paper/2307.04090/) (2023)
- [AKReF: An argumentative knowledge representation framework for structured argumentation](https://mlsanity.com/paper/2506.00713/) (2025)
- [Show Me How You Reason and I'll Tell You Who You Are: Reasoning Graphs for Robust LLM Authorship Attribution](https://mlsanity.com/paper/2607.14905/) (2026)
- [A Theory of Post-hoc Debate Judgement](https://mlsanity.com/paper/2608.19002/) (2026)
- [OmouAI: Argumentative Human-AI Policy Deliberation with Simulated Personas](https://mlsanity.com/paper/2609.31078/) (2026)
- [Have You Been Convinced? A Neuro-symbolic Argumentation Reasoning Approach to Detecting Persuasiveness in ChangeMyView Discussions](https://mlsanity.com/paper/2610.15091/) (2026)

## Abstract (arXiv)

The lack of annotated data on professional argumentation and complete argumentative debates has led to the oversimplification and the inability of approaching more complex natural language processing tasks. Such is the case of the automatic debate evaluation. In this paper, we propose an original hybrid method to automatically evaluate argumentative debates. For that purpose, we combine concepts from argumentation theory such as argumentation frameworks and semantics, with Transformer-based architectures and neural graph networks. Furthermore, we obtain promising results that lay the basis on an unexplored new instance of the automatic analysis of natural language arguments.