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Discourse Parsing On Instructional Dt Instr

Métriques

Standard Parseval (Full)
Standard Parseval (Nuclearity)
Standard Parseval (Relation)
Standard Parseval (Span)

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle
Standard Parseval (Full)
Standard Parseval (Nuclearity)
Standard Parseval (Relation)
Standard Parseval (Span)
Paper TitleRepository
Top-down (XLNet)40.255.247.074.3A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing
Bottom-up (DeBERTa)44.460.051.477.8A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing
Top-down (BERT)30.944.637.665.3A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing
Top-down (SpanBERT)36.754.542.773.7A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing
Top-down (DeBERTa)43.457.950.077.3A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing
Guz et al. (2020)-44.41-64.55Unleashing the Power of Neural Discourse Parsers -- A Context and Structure Aware Approach Using Large Scale Pretraining-
Bottom-up (SpanBERT)40.553.846.072.9A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing
Top-down (RoBERTa)41.556.148.775.7A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing
Bottom-up (BERT)32.946.339.566.6A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing
Guz et al. (2020) (pretrained)-46.59-65.41Unleashing the Power of Neural Discourse Parsers -- A Context and Structure Aware Approach Using Large Scale Pretraining-
Bottom-up (XLNet)40.756.447.473.6A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing
Bottom-up (RoBERTa)41.455.547.973.2A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing
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