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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 31–40 of 172 papers

Title	Date	Tasks	Status	Hype
Evaluating the Effectiveness of Linguistic Knowledge in Pretrained Language Models: A Case Study of Universal Dependencies	Jun 5, 2025	Paraphrase Identification	—Unverified	0
AMRITA\_CEN@SemEval-2015: Paraphrase Detection for Twitter using Unsupervised Feature Learning with Recursive Autoencoders	Jun 1, 2015	Information RetrievalMachine Translation	—Unverified	0
Balanced Adversarial Training: Balancing Tradeoffs Between Oversensitivity and Undersensitivity in NLP Models	Jan 16, 2022	Contrastive LearningNatural Language Inference	—Unverified	0
A Cross-Sentence Latent Variable Model for Semi-Supervised Text Sequence Matching	Jun 4, 2019	DecoderNatural Language Inference	—Unverified	0
Element-wise Bilinear Interaction for Sentence Matching	Jun 1, 2018	Natural Language InferenceParaphrase Identification	—Unverified	0
Experiments on Paraphrase Identification Using Quora Question Pairs Dataset	Jun 4, 2020	Binary ClassificationParaphrase Identification	—Unverified	0
AWE: Asymmetric Word Embedding for Textual Entailment	Sep 11, 2018	Natural Language InferenceParaphrase Identification	—Unverified	0
A Unified Kernel Approach for Learning Typed Sentence Rewritings	Jul 1, 2015	Machine TranslationNatural Language Inference	—Unverified	0
Discriminative Improvements to Distributional Sentence Similarity	Oct 1, 2013	Machine TranslationParaphrase Identification	—Unverified	0
Contextualized Embeddings based Convolutional Neural Networks for Duplicate Question Identification	Sep 3, 2021	Paraphrase IdentificationQuestion Answering	—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