Task	Dataset	Model	Metric name	Metric value	Global rank
Sentiment Analysis	CR	USE_T+CNN (w2v w.e.)	Accuracy	87.45	# 2
Sentiment Analysis	MPQA	USE_T+DAN (w2v w.e.)	Accuracy	88.14	# 1
Sentiment Analysis	MR	USE_T+CNN	Accuracy	81.59	# 5
Sentiment Analysis	SST-2 Binary classification	USE_T+CNN (lrn w.e.)	Accuracy	87.21	# 16
Semantic Textual Similarity	STS Benchmark	USE_T	Pearson Correlation	0.782	# 1
Subjectivity Analysis	SUBJ	USE	Accuracy	93.90	# 4
Text Classification	TREC-6	USE_T+CNN	Error	1.93	# 1

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Universal Sentence Encoder

29 Mar 2018 • Daniel Cer • Yinfei Yang • Sheng-yi Kong • Nan Hua • Nicole Limtiaco • Rhomni St. John • Noah Constant • Mario Guajardo-Cespedes • Steve Yuan • Chris Tar • Yun-Hsuan Sung • Brian Strope • Ray Kurzweil

We present models for encoding sentences into embedding vectors that specifically target transfer learning to other NLP tasks. The models are efficient and result in accurate performance on diverse transfer tasks... (read more)

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facebookresearch/InferSent

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facebookresearch/SentEval

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ncbi-nlp/BioSentVec

korymath/jann

ncbi-nlp/BioWordVec

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SEMANTIC TEXTUAL SIMILARITY

SENTENCE EMBEDDINGS

SENTIMENT ANALYSIS

SUBJECTIVITY ANALYSIS

TEXT CLASSIFICATION

TRANSFER LEARNING

WORD EMBEDDINGS

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SOTA for Text Classification on TREC-6

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Task	Dataset	Model	Metric name	Metric value	Global rank	Compare
Sentiment Analysis	CR	USE_T+CNN (w2v w.e.)	Accuracy	87.45	# 2	See all
Sentiment Analysis	MPQA	USE_T+DAN (w2v w.e.)	Accuracy	88.14	# 1	See all
Sentiment Analysis	MR	USE_T+CNN	Accuracy	81.59	# 5	See all
Sentiment Analysis	SST-2 Binary classification	USE_T+CNN (lrn w.e.)	Accuracy	87.21	# 16	See all
Semantic Textual Similarity	STS Benchmark	USE_T	Pearson Correlation	0.782	# 1	See all
Subjectivity Analysis	SUBJ	USE	Accuracy	93.90	# 4	See all
Text Classification	TREC-6	USE_T+CNN	Error	1.93	# 1	See all

Universal Sentence Encoder

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