# Aspect-Controlled Neural Argument Generation

arXiv: https://arxiv.org/abs/2005.00084 · HTML: https://mlsanity.com/paper/2005.00084/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2020.

## Built on by (most cited first, 30 of 43)

- [Tailor: Generating and Perturbing Text with Semantic Controls](https://mlsanity.com/paper/2107.07150/) (2021)
- [ERNIE 3.0 Titan: Exploring Larger-scale Knowledge Enhanced Pre-training for Language Understanding and Generation](https://mlsanity.com/paper/2112.12731/) (2021)
- [Exploring the Potential of Large Language Models in Computational Argumentation](https://mlsanity.com/paper/2311.09022/) (2023)
- [Reinforcement Learning-based Counter-Misinformation Response Generation: A Case Study of COVID-19 Vaccine Misinformation](https://mlsanity.com/paper/2303.06433/) (2023)
- [Metaphor Generation with Conceptual Mappings](https://mlsanity.com/paper/2106.01228/) (2021)
- [PLANET: Dynamic Content Planning in Autoregressive Transformers for Long-form Text Generation](https://mlsanity.com/paper/2203.09100/) (2022)
- [Argument Undermining: Counter-Argument Generation by Attacking Weak Premises](https://mlsanity.com/paper/2105.11752/) (2021)
- [The Moral Debater: A Study on the Computational Generation of Morally Framed Arguments](https://mlsanity.com/paper/2203.14563/) (2022)
- [Scientia Potentia Est -- On the Role of Knowledge in Computational Argumentation](https://mlsanity.com/paper/2107.00281/) (2021)
- [Key Point Analysis via Contrastive Learning and Extractive Argument Summarization](https://mlsanity.com/paper/2109.15086/) (2021)
- [Self-conditioning pre-trained language models](https://mlsanity.com/paper/2110.02802/) (2021)
- [Belief-based Generation of Argumentative Claims](https://mlsanity.com/paper/2101.09765/) (2021)
- [Generating Informative Conclusions for Argumentative Texts](https://mlsanity.com/paper/2106.01064/) (2021)
- [Argue with Me Tersely: Towards Sentence-Level Counter-Argument Generation](https://mlsanity.com/paper/2312.13608/) (2023)
- [ENTRUST: Argument Reframing with Language Models and Entailment](https://mlsanity.com/paper/2103.06758/) (2021)
- [AMERICANO: Argument Generation with Discourse-driven Decomposition and Agent Interaction](https://mlsanity.com/paper/2310.20352/) (2023)
- ["What makes a question inquisitive?" A Study on Type-Controlled Inquisitive Question Generation](https://mlsanity.com/paper/2205.08056/) (2022)
- [Controlled Language Generation for Language Learning Items](https://mlsanity.com/paper/2211.15731/) (2022)
- [ArgU: A Controllable Factual Argument Generator](https://mlsanity.com/paper/2305.05334/) (2023)
- [A Logical Fallacy-Informed Framework for Argument Generation](https://mlsanity.com/paper/2408.03618/) (2024)
- [AutoPersuade: A Framework for Evaluating and Explaining Persuasive Arguments](https://mlsanity.com/paper/2410.08917/) (2024)
- [Similarity-weighted Construction of Contextualized Commonsense Knowledge Graphs for Knowledge-intense Argumentation Tasks](https://mlsanity.com/paper/2305.08495/) (2023)
- [NLAS-multi: A Multilingual Corpus of Automatically Generated Natural Language Argumentation Schemes](https://mlsanity.com/paper/2402.14458/) (2024)
- [MODS: Moderating a Mixture of Document Speakers to Summarize Debatable Queries in Document Collections](https://mlsanity.com/paper/2502.00322/) (2025)
- [Argument Summarization and its Evaluation in the Era of Large Language Models](https://mlsanity.com/paper/2503.00847/) (2025)
- [ArgInstruct: Specialized Instruction Fine-Tuning for Computational Argumentation](https://mlsanity.com/paper/2505.22076/) (2025)
- [Claim Optimization in Computational Argumentation](https://mlsanity.com/paper/2212.08913/) (2022)
- [Dynamic Knowledge Integration for Evidence-Driven Counter-Argument Generation with Large Language Models](https://mlsanity.com/paper/2503.05328/) (2025)
- [Emotionally Charged, Logically Blurred: AI-driven Emotional Framing Impairs Human Fallacy Detection](https://mlsanity.com/paper/2510.09695/) (2025)
- [Prompt, Condition, and Generate: Classification of Unsupported Claims with In-Context Learning](https://mlsanity.com/paper/2309.10359/) (2023)

## Abstract (arXiv)

We rely on arguments in our daily lives to deliver our opinions and base them on evidence, making them more convincing in turn. However, finding and formulating arguments can be challenging. In this work, we train a language model for argument generation that can be controlled on a fine-grained level to generate sentence-level arguments for a given topic, stance, and aspect. We define argument aspect detection as a necessary method to allow this fine-granular control and crowdsource a dataset with 5,032 arguments annotated with aspects. Our evaluation shows that our generation model is able to generate high-quality, aspect-specific arguments. Moreover, these arguments can be used to improve the performance of stance detection models via data augmentation and to generate counter-arguments. We publish all datasets and code to fine-tune the language model.