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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 131–140 of 172 papers

Title	Date	Tasks	Status	Hype	Score
TRANS-BLSTM: Transformer with Bidirectional LSTM for Language Understanding	Mar 16, 2020	Machine TranslationNatural Language Inference	—Unverified	0	0
An Optimal Quadratic Approach to Monolingual Paraphrase Alignment	May 1, 2015	Combinatorial OptimizationMachine Translation	—Unverified	0	0
An Exploration of Embeddings for Generalized Phrases	Jun 1, 2014	Coreference ResolutionParaphrase Identification	—Unverified	0	0
Semantic Sentence Matching with Densely-connected Recurrent and Co-attentive Information	May 29, 2018	Natural Language InferenceParaphrase Identification	—Unverified	0	0
Semantic Similarity Analysis for Paraphrase Identification in Arabic Texts	Nov 1, 2017	Paraphrase IdentificationSemantic Similarity	—Unverified	0	0
Contextualized Embeddings based Convolutional Neural Networks for Duplicate Question Identification	Sep 3, 2021	Paraphrase IdentificationQuestion Answering	—Unverified	0	0
SemEval-2013 Task 5: Evaluating Phrasal Semantics	Jun 1, 2013	Paraphrase IdentificationSemantic Textual Similarity	—Unverified	0	0
Co-Stack Residual Affinity Networks with Multi-level Attention Refinement for Matching Text Sequences	Oct 6, 2018	Paraphrase IdentificationQuestion Answering	—Unverified	0	0
SemEval-2015 Task 1: Paraphrase and Semantic Similarity in Twitter (PIT)	Jun 1, 2015	Information RetrievalParaphrase Identification	—Unverified	0	0
Cross-Lingual Adaptation Using Universal Dependencies	Mar 24, 2020	Paraphrase IdentificationRelation Extraction	—Unverified	0	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