| Challenge: | Discourse structure is integral to understanding a text and is useful in many NLP tasks. |
| Approach: | They propose a structured attention mechanism for text classification that derives a tree over a text, akin to an RST discourse tree. |
| Outcome: | The proposed model improves performance on multiple discourse-relevant tasks and datasets and ablation studies show it does little to capture discourse structure. |
Similar Papers
Discourse Representation Parsing for Sentences and Documents (P19-1)
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| Challenge: | Experimental results show that our model outperforms competitive baselines by a wide margin. |
| Approach: | They propose a neural model which parses discourse structures of arbitrary length and granularity. |
| Outcome: | The proposed model outperforms baseline models on sentence- and document-level benchmarks. |
Unleashing the Power of Neural Discourse Parsers - A Context and Structure Aware Approach Using Large Scale Pretraining (2020.coling-main)
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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. |
| Outcome: | The proposed parser outperforms existing models on two key RST datasets and on large-scale "silver-standard" discourse treebank MEGA-DT. |
Discourse Structure Extraction from Pre-Trained and Fine-Tuned Language Models in Dialogues (2023.findings-eacl)
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| Challenge: | Discourse processing suffers from data sparsity, especially for dialogues . a variety of discourse frameworks have been proposed to extract discourse information from dialogues. |
| Approach: | They propose unsupervised and semi-supervised methods to infer latent discourse structures for dialogues based on attention matrices from Pre-trained Language Models. |
| Outcome: | The proposed methods achieve encouraging results on the STAC corpus, with F1 scores of 57.2 and 59.3 for the unsupervised and semi-supervised methods, respectively. |
Neural RST-based Evaluation of Discourse Coherence (2020.aacl-main)
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| 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. |
Discourse Representation Structure Parsing (P18-1)
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| Challenge: | Existing semantic parsers are data-driven using annotated examples consisting of utterances and their meaning representations. |
| Approach: | They propose a method which transforms Discourse Representation Structures (DRSs) to trees and develop a structure-aware model which decomposes the decoding process into three stages. |
| Outcome: | The proposed model outperforms baseline models on the Groningen Meaning Bank (GMB) by a wide margin. |
Enhancing Discourse Parsing for Local Structures from Social Media with LLM-Generated Data (2025.coling-main)
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| Challenge: | Existing discourse parsers do not generalize well across genres and text types. |
| Approach: | They propose to integrate large language models into RST discourse parsers to improve parser performance in a social media context. |
| Outcome: | The proposed model improves parser performance in a social media context without pre-identified discourse units. |
Integrating Tree Structures and Graph Structures with Neural Networks to Classify Discussion Discourse Acts (C18-1)
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| Challenge: | Existing models that analyze textual contents and discussion structures require understanding of textual content and discussion structure. |
| Approach: | They propose a model that integrates discussion structures with neural networks to classify discourse acts. |
| Outcome: | The proposed model improves accuracy and FB1 score by 1.5% compared to the previous best model. |
Predicting Discourse Trees from Transformer-based Neural Summarizers (2021.naacl-main)
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| Challenge: | Existing extractive summarization tasks use only neural approaches to learn discourse information, but recent work has shown that it is beneficial for summarizing discourse information. |
| Approach: | They propose to generate document-level discourse trees from pre-trained neural summarizers that encode dependency- and constituency-style discourse information. |
| Outcome: | The proposed model learns both, dependency- and constituency-style discourse information, consistent with pre-neural results. |
A Unified Linear-Time Framework for Sentence-Level Discourse Parsing (P19-1)
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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 . |
| Approach: | They propose a neural framework for sentence-level discourse analysis in accordance with Rhetorical Structure Theory . they use a discourse segmenter and a parser to construct a discursive tree in a top-down fashion . |
| Outcome: | The proposed framework surpasses previous approaches on both tasks and human agreement on both. |
DRTS Parsing with Structure-Aware Encoding and Decoding (2020.acl-main)
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| Challenge: | Discourse representation tree structure (DRTS) parsing is a new semantic parser which ignores structural information. |
| Approach: | They propose a structural-aware model to integrate structural information into the model . they use graph attention network (GAT) to exploit structural information for effective modeling . |
| Outcome: | The proposed model can achieve the best performance on a benchmark dataset. |