Window-based neural tagging for shallow discourse argument labeling

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Abstract

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.

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Knaebel, R., Stede, M., & Stober, S. (2019). Window-based neural tagging for shallow discourse argument labeling. In CoNLL 2019 - 23rd Conference on Computational Natural Language Learning, Proceedings of the Conference (pp. 768–777). Association for Computational Linguistics. https://doi.org/10.18653/v1/k19-1072

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