# Classifying Argumentative Relations Using Logical Mechanisms and Argumentation Schemes

arXiv: https://arxiv.org/abs/2105.07571 · HTML: https://mlsanity.com/paper/2105.07571/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 56 references that are in the map.
- First posted on arXiv 17 May 2021.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [Probing Neural Network Comprehension of Natural Language Arguments](https://mlsanity.com/paper/1907.07355/) (2019)
- [SemEval-2017 Task 4: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.00741/) (2019)
- [Parsing Argumentation Structures in Persuasive Essays](https://mlsanity.com/paper/1604.07370/) (2016)
- [Zero-Shot Stance Detection: A Dataset and Model using Generalized Topic Representations](https://mlsanity.com/paper/2010.03640/) (2020)
- [WIQA: A dataset for "What if..." reasoning over procedural text](https://mlsanity.com/paper/1909.04739/) (2019)
- [Argumentation Mining in User-Generated Web Discourse](https://mlsanity.com/paper/1601.02403/) (2016)
- [Hinge-Loss Markov Random Fields and Probabilistic Soft Logic](https://mlsanity.com/paper/1505.04406/) (2015)
- [Cross-topic Argument Mining from Heterogeneous Sources Using Attention-based Neural Networks](https://mlsanity.com/paper/1802.05758/) (2018)
- [Classification and Clustering of Arguments with Contextualized Word Embeddings](https://mlsanity.com/paper/1906.09821/) (2019)
- [Neural End-to-End Learning for Computational Argumentation Mining](https://mlsanity.com/paper/1704.06104/) (2017)
- [Cross-Target Stance Classification with Self-Attention Networks](https://mlsanity.com/paper/1805.06593/) (2018)
- [AMPERSAND: Argument Mining for PERSuAsive oNline Discussions](https://mlsanity.com/paper/2004.14677/) (2020)
- [Distinguishing Antonyms and Synonyms in a Pattern-based Neural Network](https://mlsanity.com/paper/1701.02962/) (2017)
- [Dissecting Content and Context in Argumentative Relation Analysis](https://mlsanity.com/paper/1906.03338/) (2019)
- [Determining Relative Argument Specificity and Stance for Complex Argumentative Structures](https://mlsanity.com/paper/1906.11313/) (2019)
- [Recognising Agreement and Disagreement between Stances with Reason Comparing Networks](https://mlsanity.com/paper/1906.01392/) (2019)

## Built on by (most cited first, 18 of 18)

- [The Causal News Corpus: Annotating Causal Relations in Event Sentences from News](https://mlsanity.com/paper/2204.11714/) (2022)
- [Can Large Language Models perform Relation-based Argument Mining?](https://mlsanity.com/paper/2402.11243/) (2024)
- [ArgU: A Controllable Factual Argument Generator](https://mlsanity.com/paper/2305.05334/) (2023)
- [Knowledge-Enhanced Evidence Retrieval for Counterargument Generation](https://mlsanity.com/paper/2109.09057/) (2021)
- [Contextual information integration for stance detection via cross-attention](https://mlsanity.com/paper/2211.01874/) (2022)
- [Computational Argumentation-based Chatbots: a Survey](https://mlsanity.com/paper/2401.03454/) (2024)
- [NLAS-multi: A Multilingual Corpus of Automatically Generated Natural Language Argumentation Schemes](https://mlsanity.com/paper/2402.14458/) (2024)
- [Event Causality Identification with Causal News Corpus -- Shared Task 3, CASE 2022](https://mlsanity.com/paper/2211.12154/) (2022)
- [Critical Questions Generation: Motivation and Challenges](https://mlsanity.com/paper/2410.14335/) (2024)
- [AMELIA: A Family of Multi-task End-to-end Language Models for Argumentation](https://mlsanity.com/paper/2508.17926/) (2025)
- [LPAttack: A Feasible Annotation Scheme for Capturing Logic Pattern of Attacks in Arguments](https://mlsanity.com/paper/2204.01512/) (2022)
- [Unknown Script: Impact of Script on Cross-Lingual Transfer](https://mlsanity.com/paper/2404.18810/) (2024)
- [IDIAPers @ Causal News Corpus 2022: Extracting Cause-Effect-Signal Triplets via Pre-trained Autoregressive Language Model](https://mlsanity.com/paper/2209.03891/) (2022)
- [Towards Comprehensive Argument Analysis in Education: Dataset, Tasks, and Method](https://mlsanity.com/paper/2505.12028/) (2025)
- [Exploration of Marker-Based Approaches in Argument Mining through Augmented Natural Language](https://mlsanity.com/paper/2406.08606/) (2024)
- [DayDreamer at CQs-Gen 2025: Generating Critical Questions through Argument Scheme Completion](https://mlsanity.com/paper/2505.15554/) (2025)
- [Designing and Analysing Argument Mining Pipelines: Towards a Comprehensive Assessment](https://mlsanity.com/paper/2609.26338/) (2026)
- [UZHCL_for_the_win at Touche: Encoder Fine-Tuning, Synthetic Augmentation, and an Underpowered Encoder-LLM Comparison](https://mlsanity.com/paper/2610.17077/) (2026)

## Abstract (arXiv)

While argument mining has achieved significant success in classifying argumentative relations between statements (support, attack, and neutral), we have a limited computational understanding of logical mechanisms that constitute those relations. Most recent studies rely on black-box models, which are not as linguistically insightful as desired. On the other hand, earlier studies use rather simple lexical features, missing logical relations between statements. To overcome these limitations, our work classifies argumentative relations based on four logical and theory-informed mechanisms between two statements, namely (i) factual consistency, (ii) sentiment coherence, (iii) causal relation, and (iv) normative relation. We demonstrate that our operationalization of these logical mechanisms classifies argumentative relations without directly training on data labeled with the relations, significantly better than several unsupervised baselines. We further demonstrate that these mechanisms also improve supervised classifiers through representation learning.