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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 71–80 of 172 papers

Title	Date	Tasks	Status	Hype
ASOBEK at SemEval-2016 Task 1: Sentence Representation with Character N-gram Embeddings for Semantic Textual Similarity	Jun 1, 2016	Language ModelingLanguage Modelling	—Unverified	0
Improving Large-scale Paraphrase Acquisition and Generation	Oct 6, 2022	Language ModelingLanguage Modelling	—Unverified	0
Combining Shallow and Deep Representations for Text-Pair Classification	Dec 1, 2021	ClassificationDecoder	—Unverified	0
A Qualitative Evaluation Framework for Paraphrase Identification	Sep 1, 2019	Paraphrase Identification	—Unverified	0
LadRa-Net: Locally-Aware Dynamic Re-read Attention Net for Sentence Semantic Matching	Aug 6, 2021	Language ModellingNatural Language Inference	—Unverified	0
Inducing Alignment Structure with Gated Graph Attention Networks for Sentence Matching	Oct 15, 2020	Graph AttentionParaphrase Identification	—Unverified	0
LCQMC:A Large-scale Chinese Question Matching Corpus	Aug 1, 2018	Information RetrievalMachine Translation	—Unverified	0
Inter-Weighted Alignment Network for Sentence Pair Modeling	Sep 1, 2017	Machine TranslationNatural Language Inference	—Unverified	0
FBK HLT-MT at SemEval-2016 Task 1: Cross-lingual Semantic Similarity Measurement Using Quality Estimation Features and Compositional Bilingual Word Embeddings	Jun 1, 2016	Cross-Lingual Semantic Textual SimilarityMachine Translation	—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