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 151–172 of 172 papers

Title	Date	Tasks	Status
Learning to Represent Bilingual Dictionaries	Aug 10, 2018	Multi-Task LearningParaphrase Identification	CodeCode Available
Memory-efficient Stochastic methods for Memory-based Transformers	Nov 14, 2023	Language ModelingLanguage Modelling	CodeCode Available
Modelling Sentence Pairs with Tree-structured Attentive Encoder	Oct 10, 2016	Paraphrase IdentificationQuestion Selection	CodeCode Available
Multi-Task Deep Neural Networks for Natural Language Understanding	Jan 31, 2019	Domain AdaptationLanguage Modeling	CodeCode Available
TinyBERT: Distilling BERT for Natural Language Understanding	Sep 23, 2019	Knowledge DistillationLanguage Modelling	CodeCode Available
Natural Language Inference over Interaction Space	Sep 13, 2017	Natural Language InferenceParaphrase Identification	CodeCode Available
Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering	Jun 12, 2018	Natural Language InferenceParaphrase Identification	CodeCode Available
Paraphrase Thought: Sentence Embedding Module Imitating Human Language Recognition	Aug 16, 2018	Document ClassificationGeneral Classification	CodeCode Available
PAWS: Paraphrase Adversaries from Word Scrambling	Apr 1, 2019	Paraphrase IdentificationSentence	CodeCode Available
Pay Attention when Required	Sep 9, 2020	Language ModellingParaphrase Identification	CodeCode Available
Recurrent Neural Network-Based Semantic Variational Autoencoder for Sequence-to-Sequence Learning	Feb 9, 2018	ImputationLanguage Modeling	CodeCode Available
RuPAWS: A Russian Adversarial Dataset for Paraphrase Identification	Jun 1, 2022	Paraphrase Identification	CodeCode Available
Sentence Similarity Learning by Lexical Decomposition and Composition	Feb 23, 2016	Paraphrase IdentificationQuestion Answering	CodeCode Available
Simple and Effective Text Matching with Richer Alignment Features	Aug 1, 2019	Answer SelectionNatural Language Inference	CodeCode Available
SpanBERT: Improving Pre-training by Representing and Predicting Spans	Jul 24, 2019	Coreference ResolutionLinguistic Acceptability	CodeCode Available
SplitEE: Early Exit in Deep Neural Networks with Split Computing	Sep 17, 2023	Natural Language InferenceParaphrase Identification	CodeCode Available
To Attend or not to Attend: A Case Study on Syntactic Structures for Semantic Relatedness	Jul 1, 2018	Machine TranslationParaphrase Identification	CodeCode Available
Tougher Text, Smarter Models: Raising the Bar for Adversarial Defence Benchmarks	Jan 5, 2025	Adversarial RobustnessBenchmarking	CodeCode Available
Towards Better Characterization of Paraphrases	Sep 17, 2021	Data AugmentationMRPC	CodeCode Available
Towards Better Characterization of Paraphrases	May 1, 2022	Data AugmentationMRPC	CodeCode Available
Training Complex Models with Multi-Task Weak Supervision	Oct 5, 2018	Matrix CompletionNatural Language Inference	CodeCode Available
Transfer Fine-Tuning: A BERT Case Study	Sep 3, 2019	Natural Language UnderstandingParaphrase Identification	CodeCode Available

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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