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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 71–80 of 172 papers

Title	Date	Tasks	Status	Hype	Score
Idiom Paraphrases: Seventh Heaven vs Cloud Nine	Sep 1, 2015	Natural Language InferenceParaphrase Identification	CodeCode Available	0	5
Co-Driven Recognition of Semantic Consistency via the Fusion of Transformer and HowNet Sememes Knowledge	Feb 21, 2023	Paraphrase IdentificationSentence	CodeCode Available	0	5
Match-Prompt: Improving Multi-task Generalization Ability for Neural Text Matching via Prompt Learning	Apr 6, 2022	Information RetrievalParaphrase Identification	CodeCode Available	0	5
Learning to Represent Bilingual Dictionaries	Aug 10, 2018	Multi-Task LearningParaphrase Identification	CodeCode Available	0	5
Modelling Sentence Pairs with Tree-structured Attentive Encoder	Oct 10, 2016	Paraphrase IdentificationQuestion Selection	CodeCode Available	0	5
Natural Language Inference over Interaction Space	Sep 13, 2017	Natural Language InferenceParaphrase Identification	CodeCode Available	0	5
Explaining Predictive Uncertainty by Looking Back at Model Explanations	Jan 11, 2022	Decision MakingNatural Language Inference	—Unverified	0	0
Bridging the Gap between Relevance Matching and Semantic Matching for Short Text Similarity Modeling	Nov 1, 2019	Information RetrievalParaphrase Identification	—Unverified	0	0
Experiments on Paraphrase Identification Using Quora Question Pairs Dataset	Jun 4, 2020	Binary ClassificationParaphrase Identification	—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

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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