SOTAVerified|Agents Browse Leaderboard About Blog

Paraphrase Identification

The goal of Paraphrase Identification is to determine whether a pair of sentences have the same meaning.

Source: Adversarial Examples with Difficult Common Words for Paraphrase Identification

Image source: On Paraphrase Identification Corpora

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 91–100 of 172 papers

Title	Date	Tasks	Status	Hype
A Cross-Sentence Latent Variable Model for Semi-Supervised Text Sequence Matching	Jun 4, 2019	DecoderNatural Language Inference	—Unverified	0
Unsupervised Paraphrasing without Translation	May 29, 2019	Machine TranslationParaphrase Identification	—Unverified	0
Adaptation of Deep Bidirectional Multilingual Transformers for Russian Language	May 17, 2019	Natural Language InferenceParaphrase Identification	CodeCode Available	0
ERNIE: Enhanced Language Representation with Informative Entities	May 17, 2019	Entity LinkingEntity Typing	CodeCode Available	0
PAWS: Paraphrase Adversaries from Word Scrambling	Apr 1, 2019	Paraphrase IdentificationSentence	CodeCode Available	0
Multiresolution Graph Attention Networks for Relevance Matching	Feb 27, 2019	Graph AttentionInformation Retrieval	—Unverified	0
Multi-Task Deep Neural Networks for Natural Language Understanding	Jan 31, 2019	Domain AdaptationLanguage Modeling	CodeCode Available	0
Learning to Selectively Transfer: Reinforced Transfer Learning for Deep Text Matching	Dec 30, 2018	Information RetrievalNatural Language Inference	—Unverified	0
Co-Stack Residual Affinity Networks with Multi-level Attention Refinement for Matching Text Sequences	Oct 6, 2018	Paraphrase IdentificationQuestion Answering	—Unverified	0
Training Complex Models with Multi-Task Weak Supervision	Oct 5, 2018	Matrix CompletionNatural Language Inference	CodeCode Available	0

Show:10 25 50

← PrevPage 10 of 18Next →

All datasets Quora Question Pairs MSRP Quora Question Pairs Dev 2017_test set AP IMDb PIT Translated SNLI Dataset in Marathi TURL WikiHop Yelp

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	BERT-Base	Direct Intrinsic Dimension	9,295	—	Unverified
2	data2vec	Accuracy	92.4	—	Unverified
3	SMART-BERT	Dev Accuracy	91.5	—	Unverified
4	ALICE	F1	90.7	—	Unverified
5	MFAE	Accuracy	90.54	—	Unverified
6	RoBERTa-large 355M + Entailment as Few-shot Learner	F1	89.2	—	Unverified
7	MwAN	Accuracy	89.12	—	Unverified
8	DIIN	Accuracy	89.06	—	Unverified
9	MSEM	Accuracy	88.86	—	Unverified
10	Bi-CAS-LSTM	Accuracy	88.6	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FEAT2, TFKLD, SVM, Fine-grained features	Accuracy	80.41	—	Unverified
2	NMF factorization-unigrams-TFKLD	Accuracy	72.75	—	Unverified
3	SWEM-concat	Accuracy	71.5	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BERT + SCH attm	Val Accuracy	91.42	—	Unverified
2	BERT + SCH attn	Val F1 Score	88.44	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CNN	10 fold Cross validation	50	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	RoBETRa base	MCC	0.53	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SplitEE-S	Accuracy	82.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	TSDAE	AP	69.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Weighted Ensemble of TF-IDF and BERT Embeddings	1:1 Accuracy	82.04	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	TSDAE	AP	76.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	StructBERTRoBERTa ensemble	Accuracy	90.7	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SplitEE-S	Accuracy	76.7	—	Unverified