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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 11–20 of 172 papers

Title	Date	Tasks	Status	Hype
Is Prompt-Based Finetuning Always Better than Vanilla Finetuning? Insights from Cross-Lingual Language Understanding	Jul 15, 2023	Cross-Lingual TransferNatural Language Inference	CodeCode Available	0
Assessing Word Importance Using Models Trained for Semantic Tasks	May 31, 2023	Natural Language InferenceParaphrase Identification	CodeCode Available	0
Cross-functional Analysis of Generalisation in Behavioural Learning	May 22, 2023	Paraphrase IdentificationReading Comprehension	CodeCode Available	0
SMoA: Sparse Mixture of Adapters to Mitigate Multiple Dataset Biases	Feb 28, 2023	Adversarial RobustnessNatural Language Inference	—Unverified	0
Co-Driven Recognition of Semantic Consistency via the Fusion of Transformer and HowNet Sememes Knowledge	Feb 21, 2023	Paraphrase IdentificationSentence	CodeCode Available	0
Paraphrase Identification with Deep Learning: A Review of Datasets and Methods	Dec 13, 2022	Paraphrase IdentificationText Generation	—Unverified	0
Improving word mover's distance by leveraging self-attention matrix	Nov 11, 2022	Paraphrase IdentificationSemantic Similarity	CodeCode Available	1
Balanced Adversarial Training: Balancing Tradeoffs between Fickleness and Obstinacy in NLP Models	Oct 20, 2022	Contrastive LearningNatural Language Inference	CodeCode Available	0
Scaling Instruction-Finetuned Language Models	Oct 20, 2022	Coreference ResolutionCross-Lingual Question Answering	CodeCode Available	3
Towards Structure-aware Paraphrase Identification with Phrase Alignment Using Sentence Encoders	Oct 11, 2022	Paraphrase IdentificationSensitivity	—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