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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 51–60 of 172 papers

Title	Date	Tasks	Status	Hype
Contextualized Embeddings based Convolutional Neural Networks for Duplicate Question Identification	Sep 3, 2021	Paraphrase IdentificationQuestion Answering	—Unverified	0
FBK HLT-MT at SemEval-2016 Task 1: Cross-lingual Semantic Similarity Measurement Using Quality Estimation Features and Compositional Bilingual Word Embeddings	Jun 1, 2016	Cross-Lingual Semantic Textual SimilarityMachine Translation	—Unverified	0
How much pretraining data do language models need to learn syntax?	Sep 7, 2021	Dependency ParsingParaphrase Identification	—Unverified	0
Improving Large-scale Paraphrase Acquisition and Generation	Oct 6, 2022	Language ModelingLanguage Modelling	—Unverified	0
Content Selection through Paraphrase Detection: Capturing different Semantic Realisations of the Same Idea	Sep 1, 2016	Paraphrase IdentificationSemantic Textual Similarity	—Unverified	0
Constructing narrative using a generative model and continuous action policies	Sep 1, 2017	Paraphrase IdentificationQ-Learning	—Unverified	0
An Exploration of Embeddings for Generalized Phrases	Jun 1, 2014	Coreference ResolutionParaphrase Identification	—Unverified	0
Knowledge-Guided Paraphrase Identification	Nov 1, 2021	Binary ClassificationParaphrase Identification	—Unverified	0
Evaluating the Effectiveness of Linguistic Knowledge in Pretrained Language Models: A Case Study of Universal Dependencies	Jun 5, 2025	Paraphrase Identification	—Unverified	0
Assessing the Eligibility of Backtranslated Samples Based on Semantic Similarity for the Paraphrase Identification Task	Sep 1, 2021	MRPCParaphrase Identification	—Unverified	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