Window-Based Neural Tagging for Shallow Discourse Argument Labeling
René Knaebel, Manfred Stede, Sebastian Stober · 2019
This paper describes a novel approach for the task of end-to-end argument labeling in shallow discourse parsing.Our method describes a decomposition of the overall labeling task into subtasks and a general distance-based aggregation procedure.For learning these subtasks, we train a recurrent neural network and gradually replace existing components of our baseline by our model.The model is trained and evaluated on the Penn Discourse Treebank 2 corpus.While it is not as good as knowledge-intensive approaches, it clearly outperforms other models that are also trained without additional linguistic features.