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홈 뉴스 최신 연구 논문 튜토리얼 데이터셋 백과사전 SOTA LLM 모델 GPU 랭킹 컨퍼런스

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Discourse Parsing On Instructional Dt Instr

평가 지표

Standard Parseval (Full)

Standard Parseval (Nuclearity)

Standard Parseval (Relation)

Standard Parseval (Span)

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름	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 | HyperAI초신경