Joint RNN Model for Argument Component Boundary Detection
TABLE I: Boundary detection on persuasive essays.
['[EMPTY]', 'F1', 'P', 'R', 'F1-B', 'F1-I', 'F1-O']
Bi-LSTM achieves .825 macro F1 thanks to the context information captured by the LSTM layer. Adding a CRF layer to Bi-LSTM can significantly improve the performance and can achieve comparable results with the CRF method that uses a number of hand-crafted features. Note that, among all methods that do not use the ground-truth argumentative-or-not information, our joint model achieves the highest performance.