This paper evaluates the utility of Rhetorical Structure Theory ( RST ) trees and relations in discourse coherence evaluation . We show that incorporating silver-standard RST features can increase accuracy when classifying coherence . We demonstrate this through our tree-recursive neural model , namely RST-Recursive , which takes advantage of the text 's RST features produced by a state of the art RST parser . We evaluate our approach on the Grammarly Corpus for Discourse Coherence ( GCDC ) and show that when ensembled with the current state of the art , we can achieve the new state of the art accuracy on this benchmark . Furthermore , when deployed alone , RST-Recursive achieves competitive accuracy while having 62 % fewer parameters . Discourse coherence has been the subject of much research in Computational Linguistics thanks to its widespread applications ( Lai and Tetreault , 2018 ) . Most current methods can be described as either stemming from explicit representations based on the Centering Theory ( Grosz et al . , 1994 ) , or deep learning approaches that learn without the use of hand-crafted linguistic features . Our work explores a third research avenue based on the Rhetorical Structure Theory ( RST ) ( Mann and Thompson , 1988 ) . We hypothesize that texts of low / high coherence tend to adhere to different discourse structures . Thus , we pose that using even silver-standard RST features should help in separating coherent texts from incoherent ones . This stems from the definition of the coherence itselfas the writer of a document needs to follow specific rules for building a clear narrative or argument structure in which the role of each constituent of the document should be appropriate with respect * to its local and global context , and even existing discourse parsers should be able to predict a plausible structure that is consistent across all coherent documents . However , if a parser has difficulty interpreting a given document , it will be more likely to produce unrealistic trees with improbable patterns of discourse relations between constituents . This idea was first explored by Feng et al . ( 2014 ) , who followed an approach similar to Barzilay and Lapata ( 2008 ) by estimating entity transition likelihoods , but instead using discourse relations ( predicted by a state of the art discourse parser ( Feng and Hirst , 2014 ) ) that entities participate in as opposed to their grammatical roles . Their method achieved significant improvements in performance even when using silver-standard discourse trees , showing potential in the use of parsed RST features for classifying textual coherence . In this paper , we explore the usefulness of silverstandard parsed RST features in neural coherence classification . We propose two new methods , RST-Recursive and Ensemble . The former achieves reasonably good performance , only 2 % short of state of the art , while more robust with 62 % fewer parameters . The latter demonstrates the added advantage of RST features in improving classification accuracy of the existing state of the art methods by setting new state of the art performance with a modest but promising margin . This signifies that the document 's rhetorical structure is an important aspect of its perceived clarity . Naturally , this improvement in performance is bounded by the quality of parsed RST features and could increase as better discourse parsers are developed . In the future , exploring other RST-based architectures for coherence classification , as well as better RST ensemble schemes and improving RST parsing can be avenues of potentially fruitful research . Additional research on multipronged approaches that draw from Centering Theory , RST and deep learning all together can also be of value .

Challenge: Existing methods for discourse coherence are either based on explicit representations from the Centering Theory or deep learning approaches without hand-crafted linguistic features.
Approach: They propose a tree-recursive neural model with silver-standard RST features based on a hypothesis that low or high coherence adheres to different discourse structures.
Outcome: The proposed model is competitive with the state-of-the-art model while having 62% fewer parameters and further boosts are achieved when ensembled with it.