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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 41–50 of 172 papers

Title	Date	Tasks	Status	Hype
Explaining Predictive Uncertainty by Looking Back at Model Explanations	Jan 11, 2022	Decision MakingNatural Language Inference	—Unverified	0
Co-Stack Residual Affinity Networks with Multi-level Attention Refinement for Matching Text Sequences	Oct 6, 2018	Paraphrase IdentificationQuestion Answering	—Unverified	0
Exploiting Sentence Similarities for Better Alignments	Nov 1, 2016	Natural Language InferenceParaphrase Identification	—Unverified	0
Cross-lingual paraphrase identification	Jun 21, 2024	Cross-Lingual Paraphrase IdentificationParaphrase Identification	—Unverified	0
A Continuously Growing Dataset of Sentential Paraphrases	Aug 1, 2017	BenchmarkingParaphrase Identification	—Unverified	0
Deep Learning of Binary and Gradient Judgements for Semantic Paraphrase	Jan 1, 2017	Deep LearningParaphrase Identification	—Unverified	0
Contextualized Embeddings based Convolutional Neural Networks for Duplicate Question Identification	Sep 3, 2021	Paraphrase IdentificationQuestion Answering	—Unverified	0
Experiments on Paraphrase Identification Using Quora Question Pairs Dataset	Jun 4, 2020	Binary ClassificationParaphrase Identification	—Unverified	0
Discriminative Phrase Embedding for Paraphrase Identification	Apr 2, 2016	Paraphrase Identification	—Unverified	0
FBK-HLT: An Effective System for Paraphrase Identification and Semantic Similarity in Twitter	Jun 1, 2015	Information RetrievalMachine Translation	—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