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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
Neural Paraphrase Identification of Questions with Noisy Pretraining	Apr 15, 2017	Paraphrase Identification	—Unverified	0
Bilateral Multi-Perspective Matching for Natural Language Sentences	Feb 13, 2017	Natural Language InferenceParaphrase Identification	CodeCode Available	0
Deep Learning of Binary and Gradient Judgements for Semantic Paraphrase	Jan 1, 2017	Deep LearningParaphrase Identification	—Unverified	0
Reddit Temporal N-gram Corpus and its Applications on Paraphrase and Semantic Similarity in Social Media using a Topic-based Latent Semantic Analysis	Dec 1, 2016	AttributeInformation Retrieval	—Unverified	0
Exploiting Sentence Similarities for Better Alignments	Nov 1, 2016	Natural Language InferenceParaphrase Identification	—Unverified	0
Modelling Sentence Pairs with Tree-structured Attentive Encoder	Oct 10, 2016	Paraphrase IdentificationQuestion Selection	CodeCode Available	0
Content Selection through Paraphrase Detection: Capturing different Semantic Realisations of the Same Idea	Sep 1, 2016	Paraphrase IdentificationSemantic Textual Similarity	—Unverified	0
A Study of MatchPyramid Models on Ad-hoc Retrieval	Jun 15, 2016	Machine TranslationParaphrase Identification	CodeCode Available	0
UTA DLNLP at SemEval-2016 Task 12: Deep Learning Based Natural Language Processing System for Clinical Information Identification from Clinical Notes and Pathology Reports	Jun 1, 2016	Information RetrievalLanguage Modeling	—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

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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