Reinforcement Learning Based Argument Component Detection
This work addresses argumentation mining for natural language processing researchers, offering an incremental improvement by leveraging previously ignored features.
The paper tackled argument component detection by incorporating historical annotations into a reinforcement learning method, achieving up to 17.85% improvement over plain RL and up to 11.94% over state-of-the-art supervised learning in classification accuracy.
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%.