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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

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Showing 141–150 of 172 papers

Title	Date	Tasks	Status	Hype
FBK HLT-MT at SemEval-2016 Task 1: Cross-lingual Semantic Similarity Measurement Using Quality Estimation Features and Compositional Bilingual Word Embeddings	Jun 1, 2016	Cross-Lingual Semantic Textual SimilarityMachine Translation	—Unverified	0
Learning to Recognize Ancillary Information for Automatic Paraphrase Identification	Jun 1, 2016	Paraphrase Identification	—Unverified	0
Discriminative Phrase Embedding for Paraphrase Identification	Apr 2, 2016	Paraphrase Identification	—Unverified	0
Sentence Similarity Learning by Lexical Decomposition and Composition	Feb 23, 2016	Paraphrase IdentificationQuestion Answering	CodeCode Available	0
ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs	Dec 16, 2015	Answer SelectionNatural Language Inference	CodeCode Available	0
Learning the Impact of Machine Translation Evaluation Metrics for Semantic Textual Similarity	Sep 1, 2015	Machine TranslationParaphrase Identification	—Unverified	0
Idiom Paraphrases: Seventh Heaven vs Cloud Nine	Sep 1, 2015	Natural Language InferenceParaphrase Identification	CodeCode Available	0
Multi-Perspective Sentence Similarity Modeling with Convolutional Neural Networks	Sep 1, 2015	Feature EngineeringMachine Translation	—Unverified	0
MultiGranCNN: An Architecture for General Matching of Text Chunks on Multiple Levels of Granularity	Jul 1, 2015	Machine TranslationParaphrase Identification	—Unverified	0
A Unified Kernel Approach for Learning Typed Sentence Rewritings	Jul 1, 2015	Machine TranslationNatural Language Inference	—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