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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 61–70 of 172 papers

Title	Date	Tasks	Status	Hype	Score
Explaining Neural Network Predictions on Sentence Pairs via Learning Word-Group Masks	Apr 9, 2021	Natural Language InferenceParaphrase Identification	CodeCode Available	0	5
SpanBERT: Improving Pre-training by Representing and Predicting Spans	Jul 24, 2019	Coreference ResolutionLinguistic Acceptability	CodeCode Available	0	5
Co-Driven Recognition of Semantic Consistency via the Fusion of Transformer and HowNet Sememes Knowledge	Feb 21, 2023	Paraphrase IdentificationSentence	CodeCode Available	0	5
Is Modularity Transferable? A Case Study through the Lens of Knowledge Distillation	Mar 27, 2024	Domain AdaptationKnowledge Distillation	CodeCode Available	0	5
Memory-efficient Stochastic methods for Memory-based Transformers	Nov 14, 2023	Language ModelingLanguage Modelling	CodeCode Available	0	5
A Deep Relevance Matching Model for Ad-hoc Retrieval	Nov 23, 2017	Ad-Hoc Information RetrievalParaphrase Identification	CodeCode Available	0	5
GAPX: Generalized Autoregressive Paraphrase-Identification X	Oct 5, 2022	Paraphrase Identification	CodeCode Available	0	5
Character-based Neural Networks for Sentence Pair Modeling	May 21, 2018	Language ModelingLanguage Modelling	CodeCode Available	0	5
Match-Prompt: Improving Multi-task Generalization Ability for Neural Text Matching via Prompt Learning	Apr 6, 2022	Information RetrievalParaphrase Identification	CodeCode Available	0	5
Application Specific Compression of Deep Learning Models	Sep 9, 2024	Deep LearningModel Compression	CodeCode Available	0	5

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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