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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 151–160 of 172 papers

Title	Date	Tasks	Status	Hype
Learning to Represent Bilingual Dictionaries	Aug 10, 2018	Multi-Task LearningParaphrase Identification	CodeCode Available	0
Memory-efficient Stochastic methods for Memory-based Transformers	Nov 14, 2023	Language ModelingLanguage Modelling	CodeCode Available	0
Modelling Sentence Pairs with Tree-structured Attentive Encoder	Oct 10, 2016	Paraphrase IdentificationQuestion Selection	CodeCode Available	0
Multi-Task Deep Neural Networks for Natural Language Understanding	Jan 31, 2019	Domain AdaptationLanguage Modeling	CodeCode Available	0
TinyBERT: Distilling BERT for Natural Language Understanding	Sep 23, 2019	Knowledge DistillationLanguage Modelling	CodeCode Available	0
Natural Language Inference over Interaction Space	Sep 13, 2017	Natural Language InferenceParaphrase Identification	CodeCode Available	0
Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering	Jun 12, 2018	Natural Language InferenceParaphrase Identification	CodeCode Available	0
Paraphrase Thought: Sentence Embedding Module Imitating Human Language Recognition	Aug 16, 2018	Document ClassificationGeneral Classification	CodeCode Available	0
PAWS: Paraphrase Adversaries from Word Scrambling	Apr 1, 2019	Paraphrase IdentificationSentence	CodeCode Available	0
Pay Attention when Required	Sep 9, 2020	Language ModellingParaphrase Identification	CodeCode Available	0

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All datasets Quora Question Pairs MSRP Quora Question Pairs Dev 2017_test set AP IMDb PIT Translated SNLI Dataset in Marathi TURL WikiHop Yelp

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