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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 101–110 of 172 papers

Title	Date	Tasks	Status	Hype
The BQ Corpus: A Large-scale Domain-specific Chinese Corpus For Sentence Semantic Equivalence Identification	Oct 1, 2018	ClusteringParaphrase Identification	—Unverified	0
AWE: Asymmetric Word Embedding for Textual Entailment	Sep 11, 2018	Natural Language InferenceParaphrase Identification	—Unverified	0
Cell-aware Stacked LSTMs for Modeling Sentences	Sep 7, 2018	Machine TranslationNatural Language Inference	—Unverified	0
Paraphrase Thought: Sentence Embedding Module Imitating Human Language Recognition	Aug 16, 2018	Document ClassificationGeneral Classification	CodeCode Available	0
Learning to Represent Bilingual Dictionaries	Aug 10, 2018	Multi-Task LearningParaphrase Identification	CodeCode Available	0
LCQMC:A Large-scale Chinese Question Matching Corpus	Aug 1, 2018	Information RetrievalMachine Translation	—Unverified	0
Multiway Attention Networks for Modeling Sentence Pairs	Jul 1, 2018	Natural Language InferenceParaphrase Identification	CodeCode Available	0
To Attend or not to Attend: A Case Study on Syntactic Structures for Semantic Relatedness	Jul 1, 2018	Machine TranslationParaphrase 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
Element-wise Bilinear Interaction for Sentence Matching	Jun 1, 2018	Natural Language InferenceParaphrase Identification	—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