Discourse Parsing On Rst Dt

Metrics

RST-Parseval (Nuclearity)

RST-Parseval (Relation)

RST-Parseval (Span)

Results

Performance results of various models on this benchmark

Model Name	RST-Parseval (Nuclearity)	RST-Parseval (Relation)	RST-Parseval (Span)	Paper Title	Repository
Bottom-up Linear-chain CRF-based Parser	71.0	58.2	85.7	-	-
Top-down (XLNet)	-	-	-	A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing
Top-down Llama 2 (13B)	-	-	-	Can we obtain significant success in RST discourse parsing by using Large Language Models?
Top-down (SpanBERT)	-	-	-	A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing
Guz et al. (2020)	-	-	-	Unleashing the Power of Neural Discourse Parsers -- A Context and Structure Aware Approach Using Large Scale Pretraining	-
Re-implemented HILDA RST parser	66.6*	54.6*	82.6*	-	-
Greedy Bottom-up Parser with Syntactic Features	67.1*	55.4*	82.6*	-	-
HILDA Parser	68.4	55.3	83.0	A Novel Discourse Parser Based on Support Vector Machine Classification	-
Bottom-up Llama 2 (70B)	-	-	-	Can we obtain significant success in RST discourse parsing by using Large Language Models?
LSTM Dynamic	-	-	-	Top-down Discourse Parsing via Sequence Labelling
Two-stage Parser	72.4	59.7	86.0	A Two-Stage Parsing Method for Text-Level Discourse Analysis
Bottom-up (DeBERTa)	-	-	-	A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing
DMRST	-	-	-	Bilingual Rhetorical Structure Parsing with Large Parallel Annotations
LSTM Sequential Discourse Parser (Braud et al., 2016)	63.6*	47.7*	79.7*	Multi-view and multi-task training of RST discourse parsers
Transformer (dynamic)	-	-	-	Top-down Discourse Parsing via Sequence Labelling
Bottom-up Llama 2 (13B)	-	-	-	Can we obtain significant success in RST discourse parsing by using Large Language Models?
Top-down Span-based Parser with Silver Agreement Subtrees	74.7	62.5	86.8	Improving Neural RST Parsing Model with Silver Agreement Subtrees	-
Bottom-up (RoBERTa)	-	-	-	A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing
End-to-end Top-down (XLNet)	76.0	61.8	87.6	RST Parsing from Scratch
Bottom-up (SpanBERT)	-	-	-	A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing

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