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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

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Showing 61–70 of 172 papers

Title	Date	Tasks	Status	Hype
Bridging the Gap between Relevance Matching and Semantic Matching for Short Text Similarity Modeling	Nov 1, 2019	Information RetrievalParaphrase Identification	—Unverified	0
Explaining Predictive Uncertainty by Looking Back at Model Explanations	Jan 11, 2022	Decision MakingNatural Language Inference	—Unverified	0
LCQMC:A Large-scale Chinese Question Matching Corpus	Aug 1, 2018	Information RetrievalMachine Translation	—Unverified	0
Content Selection through Paraphrase Detection: Capturing different Semantic Realisations of the Same Idea	Sep 1, 2016	Paraphrase IdentificationSemantic Textual Similarity	—Unverified	0
Constructing narrative using a generative model and continuous action policies	Sep 1, 2017	Paraphrase IdentificationQ-Learning	—Unverified	0
Assessing the Eligibility of Backtranslated Samples Based on Semantic Similarity for the Paraphrase Identification Task	Sep 1, 2021	MRPCParaphrase Identification	—Unverified	0
Combining Shallow and Deep Representations for Text-Pair Classification	Dec 1, 2021	ClassificationDecoder	—Unverified	0
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
Robustness to Modification with Shared Words in Paraphrase Identification	Sep 5, 2019	Language ModelingLanguage Modelling	—Unverified	0
Inducing Alignment Structure with Gated Graph Attention Networks for Sentence Matching	Oct 15, 2020	Graph AttentionParaphrase Identification	—Unverified	0

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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