TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK
Natural Language Inference	SNLI	SLRC	% Test Accuracy	89.1	# 12
Natural Language Inference	SNLI	SLRC	% Train Accuracy	89.1	# 40
Natural Language Inference	SNLI	SLRC	Parameters	6.1m	# 2
Natural Language Inference	SNLI	SJRC (BERT-Large +SRL)	% Test Accuracy	91.3	# 5
Natural Language Inference	SNLI	SJRC (BERT-Large +SRL)	% Train Accuracy	95.7	# 7
Natural Language Inference	SNLI	SJRC (BERT-Large +SRL)	Parameters	308m	# 2

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Explicit Contextual Semantics for Text Comprehension

8 Sep 2018 • Zhuosheng Zhang • Yuwei Wu • Zuchao Li • Hai Zhao

Who did what to whom is a major focus in natural language understanding, which is right the aim of semantic role labeling (SRL) task. Despite of sharing a lot of processing characteristics and even task purpose, it is surprisingly that jointly considering these two related tasks was never formally reported in previous work... (read more)

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MACHINE READING COMPREHENSION

NATURAL LANGUAGE UNDERSTANDING

READING COMPREHENSION

SEMANTIC ROLE LABELING

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Ranked #5 on Natural Language Inference on SNLI

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TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK	BENCHMARK
Natural Language Inference	SNLI	SLRC	% Test Accuracy	89.1	# 12	Compare
			% Train Accuracy	89.1	# 40	Compare
			Parameters	6.1m	# 2	Compare
Natural Language Inference	SNLI	SJRC (BERT-Large +SRL)	% Test Accuracy	91.3	# 5	Compare
			% Train Accuracy	95.7	# 7	Compare
			Parameters	308m	# 2	Compare

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Explicit Contextual Semantics for Text Comprehension

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