mlsanityData to 7 Oct 2026

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

arXiv 2203.14647 ↗First posted 28 Mar 2022cs.CLcited by 10

Not on the map. Why?

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.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 12 of its 43 references that are in the map. First posted on arXiv 28 Mar 2022.

Builds on

12 of 43 references.

References · 12

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  2. 2020Longformer: The Long-Document Transformercited by 3,362
  3. 2018Relational inductive biases, deep learning, and graph networkscited by 1,341
  4. 2020Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillationcited by 591
  5. 2020From Arguments to Key Points: Towards Automatic Argument Summarizationcited by 60
  6. 2020DebateSum: A large-scale argument mining and summarization datasetcited by 34
  7. 2019Are You Convinced? Choosing the More Convincing Evidence with a Siamese Networkcited by 29
  8. 2019A Corpus for Modeling User and Language Effects in Argumentation on Online Debatingcited by 22
  9. 2020Transformer-Based Models for Automatic Identification of Argument Relations: A Cross-Domain Evaluationcited by 20
  10. 2019A Dataset of General-Purpose Rebuttalcited by 8
  11. 2020Towards an Argument Mining Pipeline Transforming Texts to Argument Graphscited by 8
  12. 2020Deep Learning for Abstract Argumentation Semanticscited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 3; 2026: 3.

10 papers.

  1. 2025Evaluating LLM-based Agents for Multi-Turn Conversations: A Surveycited by 56
  2. 2024Debatrix: Multi-dimensional Debate Judge with Iterative Chronological Analysis Based on LLMcited by 21
  3. 2024An Empirical Analysis on Large Language Models in Debate Evaluationcited by 20
  4. 2025Large Language Models in Argument Mining: A Surveycited by 14
  5. 2024A Methodology for Incompleteness-Tolerant and Modular Gradual Semantics for Argumentative Statement Graphscited by 2
  6. 2023DebateKG: Automatic Policy Debate Case Creation with Semantic Knowledge Graphscited by 0
  7. 2025AKReF: An argumentative knowledge representation framework for structured argumentationcited by 0
  8. 2026Show Me How You Reason and I'll Tell You Who You Are: Reasoning Graphs for Robust LLM Authorship Attributioncited by 0
  9. 2026A Theory of Post-hoc Debate Judgementcited by 0
  10. 2026OmouAI: Argumentative Human-AI Policy Deliberation with Simulated Personascited by 0