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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 31–40 of 172 papers

Title	Date	Tasks	Status	Hype
Match-Prompt: Improving Multi-task Generalization Ability for Neural Text Matching via Prompt Learning	Apr 6, 2022	Information RetrievalParaphrase Identification	CodeCode Available	0
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language	Feb 7, 2022	image-classificationImage Classification	CodeCode Available	1
PerPaDa: A Persian Paraphrase Dataset based on Implicit Crowdsourcing Data Collection	Jan 17, 2022	Paraphrase Identification	—Unverified	0
Balanced Adversarial Training: Balancing Tradeoffs Between Oversensitivity and Undersensitivity in NLP Models	Jan 16, 2022	Contrastive LearningNatural Language Inference	—Unverified	0
Explaining Predictive Uncertainty by Looking Back at Model Explanations	Jan 11, 2022	Decision MakingNatural Language Inference	—Unverified	0
BnPC: A Corpus for Paraphrase Detection in Bangla	Dec 17, 2021	Paraphrase IdentificationSentence	—Unverified	0
Combining Shallow and Deep Representations for Text-Pair Classification	Dec 1, 2021	ClassificationDecoder	—Unverified	0
Predicate-Argument Based Bi-Encoder for Paraphrase Identification	Nov 16, 2021	Paraphrase IdentificationSentence	—Unverified	0
Knowledge-Guided Paraphrase Identification	Nov 1, 2021	Binary ClassificationParaphrase Identification	—Unverified	0
Trans-Encoder: Unsupervised sentence-pair modelling through self- and mutual-distillations	Sep 27, 2021	Contrastive LearningLanguage Modelling	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