Neural RST-based Evaluation of Discourse Coherence
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 R...
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Zusammenfassung: | 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. |
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DOI: | 10.48550/arxiv.2009.14463 |