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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 Title	Repository
Top-down (XLNet)	40.2	55.2	47.0	74.3	A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing	-
Bottom-up (DeBERTa)	44.4	60.0	51.4	77.8	A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing	-
Top-down (BERT)	30.9	44.6	37.6	65.3	A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing	-
Top-down (SpanBERT)	36.7	54.5	42.7	73.7	A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing	-
Top-down (DeBERTa)	43.4	57.9	50.0	77.3	A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing	-
Guz et al. (2020)	-	44.41	-	64.55	Unleashing the Power of Neural Discourse Parsers -- A Context and Structure Aware Approach Using Large Scale Pretraining	-
Bottom-up (SpanBERT)	40.5	53.8	46.0	72.9	A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing	-
Top-down (RoBERTa)	41.5	56.1	48.7	75.7	A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing	-
Bottom-up (BERT)	32.9	46.3	39.5	66.6	A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing	-
Guz et al. (2020) (pretrained)	-	46.59	-	65.41	Unleashing the Power of Neural Discourse Parsers -- A Context and Structure Aware Approach Using Large Scale Pretraining	-
Bottom-up (XLNet)	40.7	56.4	47.4	73.6	A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing	-
Bottom-up (RoBERTa)	41.4	55.5	47.9	73.2	A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing	-

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Discourse Parsing On Instructional Dt Instr | SOTA | HyperAI