Towards an Implementation of R hetorical S tructure T heory in Discourse Coherence Modelling (2024)

Abstract: In this paper, we combine the discourse coherence principles of Elementary Discourse Unit segmentation and Rhetorical Structure Theory parsing to construct meaningful graph-based text representations. We then evaluate a Graph Convolutional Network and a Graph Attention Network on these representations. Our results establish a new benchmark in F1-score assessment for discourse coherence modelling while also showing that Graph Convolutional Network models are generally more computationally efficient and provide superior accuracy.

## introduction natural language processing (nlp) has seen significant advancements, particularly with attentionbased transformer models excelling in tasks such as machine translation, language modelling @xcite , and sentiment analysis @xcite . however, effectively modelling discourse coherence remains a challenge, especially as long context and long form text genearation tasks become more prevalent. this research aims to address this by extending a graph-construction approach developed by @xcite , integrating the linguistically-focused principles of elementary discourse unit (edu) segmentation and rhetorical structure theory (rst) parsing into a graphbased approach using graph convolutional network (gcn) and graph attention network (gat) architectures. this graph-based approach marks a departure from typical discourse coherence assessments such as those by @xcite which treat coherence as a sentence-rearrangement task. our goal is to further the field of discourse coherence modelling, which is crucial for tasks like essay grading, mental health detection, and identifying machine-written text. ## conclusion this study has made several key contributions to the field of discourse coherence modelling: our findings have several important implications: