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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
Improving Paraphrase Detection with the Adversarial Paraphrasing Task	Jun 14, 2021	Dataset GenerationParaphrase Identification	CodeCode Available	1
Factorising Meaning and Form for Intent-Preserving Paraphrasing	May 31, 2021	DecoderForm	CodeCode Available	1
FNet: Mixing Tokens with Fourier Transforms	May 9, 2021	Linguistic AcceptabilityMachine Translation	CodeCode Available	1
Entailment as Few-Shot Learner	Apr 29, 2021	Contrastive LearningData Augmentation	CodeCode Available	1
TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning	Apr 14, 2021	DenoisingDomain Adaptation	CodeCode Available	1
Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning	Dec 22, 2020	Generalization BoundsLanguage Modeling	CodeCode Available	1
RealFormer: Transformer Likes Residual Attention	Dec 21, 2020	Language ModelingLanguage Modelling	CodeCode Available	1
Self-Explaining Structures Improve NLP Models	Dec 3, 2020	Natural Language InferenceParaphrase Identification	CodeCode Available	1
Adversarial Semantic Collisions	Nov 9, 2020	Extractive SummarizationParaphrase Identification	CodeCode Available	1
PARADE: A New Dataset for Paraphrase Identification Requiring Computer Science Domain Knowledge	Oct 8, 2020	Paraphrase Identification	CodeCode Available	1

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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