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 81–90 of 172 papers

Title	Date	Tasks	Status	Hype
Bridging the Gap between Relevance Matching and Semantic Matching for Short Text Similarity Modeling	Nov 1, 2019	Information RetrievalParaphrase Identification	—Unverified	0
Original Semantics-Oriented Attention and Deep Fusion Network for Sentence Matching	Nov 1, 2019	Natural Language InferenceParaphrase Identification	—Unverified	0
Sentence Embeddings for Russian NLU	Oct 29, 2019	Multiple-choiceParaphrase Identification	CodeCode Available	0
TinyBERT: Distilling BERT for Natural Language Understanding	Sep 23, 2019	Knowledge DistillationLanguage Modelling	CodeCode Available	0
Robustness to Modification with Shared Words in Paraphrase Identification	Sep 5, 2019	Language ModelingLanguage Modelling	—Unverified	0
Transfer Fine-Tuning: A BERT Case Study	Sep 3, 2019	Natural Language UnderstandingParaphrase Identification	CodeCode Available	0
A Qualitative Evaluation Framework for Paraphrase Identification	Sep 1, 2019	Paraphrase Identification	—Unverified	0
StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding	Aug 13, 2019	Language ModelingLanguage Modelling	—Unverified	0
Simple and Effective Text Matching with Richer Alignment Features	Aug 1, 2019	Answer SelectionNatural Language Inference	CodeCode Available	0
SpanBERT: Improving Pre-training by Representing and Predicting Spans	Jul 24, 2019	Coreference ResolutionLinguistic Acceptability	CodeCode Available	0

Show:10 25 50

← PrevPage 9 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