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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 61–70 of 172 papers

Title	Date	Tasks	Status	Hype
Task-adaptive Pre-training and Self-training are Complementary for Natural Language Understanding	Sep 14, 2021	named-entity-recognitionNamed Entity Recognition	—Unverified	0
How much pretraining data do language models need to learn syntax?	Sep 7, 2021	Dependency ParsingParaphrase Identification	—Unverified	0
Contextualized Embeddings based Convolutional Neural Networks for Duplicate Question Identification	Sep 3, 2021	Paraphrase IdentificationQuestion Answering	—Unverified	0
Does BERT Understand Idioms? A Probing-Based Empirical Study of BERT Encodings of Idioms	Sep 1, 2021	Paraphrase Identification	—Unverified	0
Towards Domain-Generalizable Paraphrase Identification by Avoiding the Shortcut Learning	Sep 1, 2021	Domain GeneralizationParaphrase Identification	—Unverified	0
Assessing the Eligibility of Backtranslated Samples Based on Semantic Similarity for the Paraphrase Identification Task	Sep 1, 2021	MRPCParaphrase Identification	—Unverified	0
Accurate, yet inconsistent? Consistency Analysis on Language Understanding Models	Aug 15, 2021	Paraphrase Identification	—Unverified	0
LadRa-Net: Locally-Aware Dynamic Re-read Attention Net for Sentence Semantic Matching	Aug 6, 2021	Language ModellingNatural Language Inference	—Unverified	0
Explaining Neural Network Predictions on Sentence Pairs via Learning Word-Group Masks	Apr 9, 2021	Natural Language InferenceParaphrase Identification	CodeCode Available	0
XLA: A Robust Unsupervised Data Augmentation Framework for Cross-Lingual NLP	Jan 1, 2021	Cross-Lingual TransferData Augmentation	—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