  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%.
