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Enhanced Rhetorical Structure Theory (eRST): A Comprehensive Framework for Discourse Analysis


Core Concepts
Enhanced Rhetorical Structure Theory (eRST) offers a comprehensive framework for discourse analysis, incorporating signals and secondary edges to enhance the representation of discourse relations.
Abstract
The article introduces eRST, an extension of Rhetorical Structure Theory (RST), focusing on computational discourse analysis. It addresses shortcomings in existing frameworks like SDRT and PDTB by introducing tree-breaking, non-projective relations, and explicit signals. The primary goal is to provide a detailed representation of discourse relations across various genres. The content covers the theoretical background, related work, formalism details, complexity considerations, data extraction from the GUM corpus, and practical applications. Directory: Introduction to eRST Presents Enhanced Rhetorical Structure Theory as a new theoretical framework for computational discourse analysis. Data Extraction from GUM Corpus Extends annotations in the English Georgetown University Multilayer corpus (GUM) covering 12 genres with over 26K EDUs. Complexity and Effort Considerations Discusses the computational complexity of eRST derivations compared to RST and addresses annotation effort challenges. Practical Applications and Future Prospects Highlights the potential applications of eRST in linguistic research and computational models.
Stats
The framework encompasses discourse relation graphs with tree-breaking, non-projective and concurrent relations. The proposed theory addresses shortcomings in existing frameworks like SDRT and PDTB using constructs in the theory. A corpus of over 200K tokens covering 12 spoken and written English text types is evaluated according to the framework. Multiple concurrent relations are supported by eRST along with hierarchical relation taxonomy. Automatic parsing is discussed along with evaluation metrics for data within the framework.
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Key Insights Distilled From

by Amir Zeldes,... at arxiv.org 03-21-2024

https://arxiv.org/pdf/2403.13560.pdf
eRST

Deeper Inquiries

How does eRST compare to other existing frameworks like SDRT and PDTB in terms of addressing shortcomings?

eRST, or Enhanced Rhetorical Structure Theory, offers improvements over existing frameworks like SDRT and PDTB by addressing some of their key shortcomings. Tree-breaking Structures: SDRT introduced the concept of tree-breaking structures to address limitations in RST's strict tree constraint. eRST incorporates this feature, allowing for non-projective relations that may not fit neatly into a hierarchical tree structure. Signal Anchoring: While PDTB focuses on explicit discourse markers as signals for relations, eRST expands this concept by incorporating various signal types beyond just connectives. This provides a more comprehensive understanding of how discourse relations are signaled in text. Hierarchical Relations: Unlike PDTB's shallow parsing approach, eRST maintains a hierarchical representation similar to RST but with added flexibility for multiple concurrent relations and secondary edges when necessary. Expressive Mechanisms: eRST introduces new features such as supporting multiple relations between nodes, maintaining recursive prominence hierarchy despite non-projectivity, and marking categorized signal types including implicit and explicit connectives. Overall, eRST combines the strengths of existing frameworks while addressing their limitations to provide a more detailed and flexible theoretical framework for computational discourse analysis.

What are some potential challenges in implementing eRST annotations manually?

Implementing eRST annotations manually can pose several challenges due to the complexity and nuances involved in analyzing discourse structures: Labor-Intensive Process: Building primary trees in Rhetorical Structure Theory (and consequently in Enhanced Rhetorical Structure Theory) is labor-intensive as it requires careful analysis of relationships between text segments. Secondary Edges Complexity: Adding secondary edges to capture additional non-projective or complex relations can increase annotation complexity significantly. Signal Detection Accuracy: Identifying and annotating signals accurately can be challenging as it requires understanding subtle linguistic cues that indicate specific discourse relationships. Consistency Across Annotators: Ensuring consistency among annotators is crucial to maintain reliability and validity of the annotated data but can be difficult due to subjective interpretations. Alignment with Primary Trees: Coordinating the annotation process between primary trees (representing main discourse structure) and secondary edges (capturing additional relationships) requires meticulous attention to detail.

How can automatic parsing be improved to reduce annotation effort while maintaining accuracy?

Automatic parsing methods can be enhanced using advanced NLP techniques to streamline the annotation process while ensuring accuracy: Machine Learning Models: Leveraging machine learning models such as neural networks for relation extraction from text data can improve efficiency by automating part of the annotation process. 2Pipeline Approach: Implementing a pipeline approach where different NLP tasks like tokenization, POS tagging, dependency parsing are integrated sequentially can help automate various aspects of parsing efficiently. 3Active Learning: Incorporating active learning strategies where models interactively query human annotators for labeling ambiguous instances helps improve model performance with minimal manual intervention. 4Semi-Supervised Learning: Utilizing semi-supervised learning techniques that leverage both labeled training data along with unlabeled data enhances model generalization capabilities without requiring extensive manual annotations. 5Integration with Pre-Trained Models: Integrating pre-trained language models like BERT or GPT-3 into automatic parsing systems enables better contextual understanding leading to more accurate results with reduced manual effort required during training phase
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