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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 81–90 of 172 papers

Title	Date	Tasks	Status	Hype
Modelling Domain Relationships for Transfer Learning on Retrieval-based Question Answering Systems in E-commerce	Nov 23, 2017	ChatbotNatural Language Inference	—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
MultiGranCNN: An Architecture for General Matching of Text Chunks on Multiple Levels of Granularity	Jul 1, 2015	Machine TranslationParaphrase Identification	—Unverified	0
Multi-Perspective Sentence Similarity Modeling with Convolutional Neural Networks	Sep 1, 2015	Feature EngineeringMachine Translation	—Unverified	0
Multiresolution Graph Attention Networks for Relevance Matching	Feb 27, 2019	Graph AttentionInformation Retrieval	—Unverified	0
Content Selection through Paraphrase Detection: Capturing different Semantic Realisations of the Same Idea	Sep 1, 2016	Paraphrase IdentificationSemantic Textual Similarity	—Unverified	0
Multi-task Sentence Encoding Model for Semantic Retrieval in Question Answering Systems	Nov 18, 2019	intent-classificationIntent Classification	—Unverified	0
An Exploration of Embeddings for Generalized Phrases	Jun 1, 2014	Coreference ResolutionParaphrase Identification	—Unverified	0
Neural Paraphrase Identification of Questions with Noisy Pretraining	Apr 15, 2017	Paraphrase Identification	—Unverified	0
On Paraphrase Identification Corpora	May 1, 2014	DiversityNatural Language Inference	—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