# Reinforcement Learning Based Argument Component Detection

arXiv: https://arxiv.org/abs/1702.06239 · HTML: https://mlsanity.com/paper/1702.06239/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2017.

## Built on by (most cited first, 1 of 1)

- [Cross-Genre Argument Mining: Can Language Models Automatically Fill in Missing Discourse Markers?](https://mlsanity.com/paper/2306.04314/) (2023)

## Abstract (arXiv)

Argument component detection (ACD) is an important sub-task in argumentation mining. ACD aims at detecting and classifying different argument components in natural language texts. Historical annotations (HAs) are important features the human annotators consider when they manually perform the ACD task. However, HAs are largely ignored by existing automatic ACD techniques. Reinforcement learning (RL) has proven to be an effective method for using HAs in some natural language processing tasks. In this work, we propose a RL-based ACD technique, and evaluate its performance on two well-annotated corpora. Results suggest that, in terms of classification accuracy, HAs-augmented RL outperforms plain RL by at most 17.85%, and outperforms the state-of-the-art supervised learning algorithm by at most 11.94%.