| Challenge: | Existing approaches to discourse parsing focus on studying the semantic and syntactic aspects of EDU pairs, but they do not address long span dependencies. |
| Approach: | They propose a new transition-based discourse parser that takes discourse cohesion into account by using memory networks. |
| Outcome: | The proposed method outperforms traditional features and improves performance on the RST discourse treebank. |
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| Challenge: | Discourse parsing is an important upstream task within the area of Natural Language Processing (NLP) . |
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| Challenge: | Experimental results show that our model outperforms competitive baselines by a wide margin. |
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| Challenge: | a new neural framework for sentence-level discourse analysis is proposed . a discourse segmenter and a parser are based on pointer networks and operate in linear time . |
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RST Parsing from Scratch (2021.naacl-main)
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| Challenge: | Fig. 1 shows a document level discourse parser that performs top-down end-to-end parsing without requiring segmentation . |
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Learning Dynamic Representations for Discourse Dependency Parsing (2023.findings-emnlp)
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| Challenge: | Existing models characterize transition states by examining a certain number of elementary discourse units (EDUs) Existing work neglects the arcs obtained from the transition history. |
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Discourse Parsing Enhanced by Discourse Dependence Perception (2022.aacl-main)
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| Challenge: | Top-down neural models still suffer from the top-down error propagation issue . previous studies gradually switch from feature-based machine learning methods to deep neural models . |
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Discourse Representation Structure Parsing (P18-1)
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A Complete Shift-Reduce Chinese Discourse Parser with Robust Dynamic Oracle (2020.acl-main)
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| Challenge: | Existing work on hierarchical discourse parsing in English is based on the RST-style one. |
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