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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 21–30 of 172 papers

Title	Date	Tasks	Status	Hype
Improving Large-scale Paraphrase Acquisition and Generation	Oct 6, 2022	Language ModelingLanguage Modelling	—Unverified	0
GAPX: Generalized Autoregressive Paraphrase-Identification X	Oct 5, 2022	Paraphrase Identification	CodeCode Available	0
Paraphrasing, textual entailment, and semantic similarity above word level	Aug 10, 2022	Natural Language InferenceParaphrase Identification	—Unverified	0
Towards Automatic Short Answer Assessment for Finnish as a Paraphrase Retrieval Task	Jul 1, 2022	Paraphrase IdentificationRetrieval	—Unverified	0
Adversarial Self-Attention for Language Understanding	Jun 25, 2022	Machine Reading ComprehensionNamed Entity Recognition (NER)	CodeCode Available	0
RuPAWS: A Russian Adversarial Dataset for Paraphrase Identification	Jun 1, 2022	Paraphrase Identification	CodeCode Available	0
Evaluating Multilingual Sentence Representation Models in a Real Case Scenario	Jun 1, 2022	Paraphrase IdentificationRetrieval	CodeCode Available	0
Towards Better Characterization of Paraphrases	May 1, 2022	Data AugmentationMRPC	CodeCode Available	0
Predicate-Argument Based Bi-Encoder for Paraphrase Identification	May 1, 2022	Paraphrase IdentificationSentence	—Unverified	0
NMTScore: A Multilingual Analysis of Translation-based Text Similarity Measures	Apr 28, 2022	Data-to-Text GenerationMachine Translation	CodeCode Available	1

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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