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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 151–160 of 172 papers

Title	Date	Tasks	Status	Hype	Score
Sentence Alignment using Unfolding Recursive Autoencoders	Aug 1, 2017	Information RetrievalMachine Translation	—Unverified	0	0
Combining Shallow and Deep Representations for Text-Pair Classification	Dec 1, 2021	ClassificationDecoder	—Unverified	0	0
Robustness to Modification with Shared Words in Paraphrase Identification	Sep 5, 2019	Language ModelingLanguage Modelling	—Unverified	0	0
Accurate, yet inconsistent? Consistency Analysis on Language Understanding Models	Aug 15, 2021	Paraphrase Identification	—Unverified	0	0
AMRITA\_CEN@SemEval-2015: Paraphrase Detection for Twitter using Unsupervised Feature Learning with Recursive Autoencoders	Jun 1, 2015	Information RetrievalMachine Translation	—Unverified	0	0
Evaluating the Effectiveness of Linguistic Knowledge in Pretrained Language Models: A Case Study of Universal Dependencies	Jun 5, 2025	Paraphrase Identification	—Unverified	0	0
Experiments on Paraphrase Identification Using Quora Question Pairs Dataset	Jun 4, 2020	Binary ClassificationParaphrase Identification	—Unverified	0	0
SMoA: Sparse Mixture of Adapters to Mitigate Multiple Dataset Biases	Feb 28, 2023	Adversarial RobustnessNatural Language Inference	—Unverified	0	0
Explaining Predictive Uncertainty by Looking Back at Model Explanations	Jan 11, 2022	Decision MakingNatural Language Inference	—Unverified	0	0
Exploiting Sentence Similarities for Better Alignments	Nov 1, 2016	Natural Language InferenceParaphrase Identification	—Unverified	0	0

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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