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Found 5 papers, 1 papers with code
Coreference for Discourse Parsing: A Neural Approach
We present preliminary results on investigating the benefits of coreference resolution features for neural RST discourse parsing by considering different levels of coupling of the discourse parser with the coreference resolver.
Unleashing the Power of Neural Discourse Parsers - A Context and Structure Aware Approach Using Large Scale Pretraining
RST-based discourse parsing is an important NLP task with numerous downstream applications, such as summarization, machine translation and opinion mining.
Ranked #10 on
Discourse Parsing
on RST-DT
(Standard Parseval (Span) metric)
Unleashing the Power of Neural Discourse Parsers -- A Context and Structure Aware Approach Using Large Scale Pretraining
RST-based discourse parsing is an important NLP task with numerous downstream applications, such as summarization, machine translation and opinion mining.
Ranked #8 on
Discourse Parsing
on RST-DT
(Standard Parseval (Span) metric)
Towards Domain-Independent Text Structuring Trainable on Large Discourse Treebanks
With the goal of fostering more general and data-driven approaches to text structuring, we propose the new and domain-independent NLG task of structuring and ordering a (possibly large) set of EDUs.
Neural RST-based Evaluation of Discourse Coherence
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.
Ranked #1 on
Coherence Evaluation
on GCDC + RST - F1
