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SOTA
语篇解析
Discourse Parsing On Rst Dt
Discourse Parsing On Rst Dt
评估指标
RST-Parseval (Nuclearity)
RST-Parseval (Relation)
RST-Parseval (Span)
评测结果
各个模型在此基准测试上的表现结果
Columns
模型名称
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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