| Challenge: | Existing discourse parsers cannot predict coherent texts without using silver-standard features. |
| Approach: | They propose a tree-recursive neural model which takes advantage of the text’s RST features produced by a state of the art RST parser and compares it to the current state of art. |
| Outcome: | The proposed model achieves state-of-the-art accuracy on the Grammarly Corpus for Discourse Coherence (GCDC) and has 62% fewer parameters than existing models. |
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| Challenge: | Discourse parsing is an important upstream task within the area of Natural Language Processing (NLP) . |
| Approach: | They propose a discourse parser that incorporates recent contextual language models to improve the performance of RST-based discourse parses. |
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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. |
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Developing a Rhetorical Structure Theory Treebank for Czech (2024.lrec-main)
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| Challenge: | a paper on the Czech RST Discourse Treebank is the first version of a textual annotation system based on the Rhetorical Structure Theory . document is annotated using the RST, a global coherence model proposed by Mann and Thompson . |
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| Challenge: | Discourse structure is integral to understanding a text and is useful in many NLP tasks. |
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How coherent are neural models of coherence? (2020.coling-main)
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| Challenge: | Existing approaches to model coherence are limited to small newswire corpora . evaluators need to be trained on lexical and document levels to perform evaluations . |
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Discourse Relation-Enhanced Neural Coherence Modeling (2025.acl-long)
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| Challenge: | Existing work on coherence modeling has focused on integrating entity-based models. |
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Modeling discourse cohesion for discourse parsing via memory network (P18-2)
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| 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. |
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Centering-based Neural Coherence Modeling with Hierarchical Discourse Segments (2020.emnlp-main)
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| Challenge: | Prior studies of coherence focused on identifying semantic relations between adjacent sentences. |
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RST-LoRA: A Discourse-Aware Low-Rank Adaptation for Long Document Abstractive Summarization (2024.naacl-long)
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| Challenge: | Existing methods to integrate rhetorical structure theory into long document summarization models are unexplored. |
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RSTGen: Imbuing Fine-Grained Interpretable Control into Long-FormText Generators (2022.naacl-main)
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| Challenge: | Using a framework based on Rhetorical Structure Theory, we aim to improve the cohesion and coherence of long-form text generated by language models. |
| Approach: | They propose a framework that utilises Rhetorical Structure Theory to control the discourse structure, semantics and topics of generated text. |
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