Papers with RST parsers
Using and comparing Rhetorical Structure Theory parsers with rst-workbench (2021.eacl-demos)
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| Challenge: | Rhetorical Structure Theory (RST) parsers are usually only trained on English data . |
| Approach: | rst-workbench is a web-based tool that lets users install and use RST parsers. |
| Outcome: | rst-workbench is a web-based tool that lets users run multiple RST parsers simultaneously. |
Improving Neural RST Parsing Model with Silver Agreement Subtrees (2021.naacl-main)
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| Challenge: | Existing methods for Rhetorical Structure Theory (RST) parsing use supervised learning, but the RST-DT is small due to the costly annotation of RST trees. |
| Approach: | They propose to use silver data to improve RST parsing models by using annotated silver data. |
| Outcome: | The proposed method achieves the best micro-F1 scores for Nuclearity and Relation at 75.0 and 63.2 . it also achieves a remarkable gain in relation score against the previous state-of-the-art parser. |
End-to-End Argument Mining over Varying Rhetorical Structures (2023.findings-acl)
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| Challenge: | Rhetorical Structure Theory implies no single discourse interpretation of a text . inconsistent parsing of similar structures can result in inconsistent argumentation analysis . |
| Approach: | They propose a deep dependency parsing model to assess the connection between rhetorical and argument structures. |
| Outcome: | The proposed model allows for end-to-end argumentation analysis using a rhetorical tree instead of a word sequence. |
Split or Merge: Which is Better for Unsupervised RST Parsing? (D19-1)
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| Challenge: | Rhetorical Structure Theory (RST) parsers have been based on supervised learning approaches that require an annotated corpus of sufficient size and quality. |
| Approach: | They propose two unsupervised methods that build an optimal RST tree based on a dissimilarity score function for splitting a text span into smaller ones and a similarity score for merging two adjacent spans into a large one. |
| Outcome: | The proposed method achieves the best score on English and German RST treebanks, around 0.8 F1 score, close to the previous supervised parsers. |