SOTAVerified

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

Showing 76100 of 172 papers

TitleStatusHype
Transfer Fine-Tuning: A BERT Case StudyCode0
Inducing Alignment Structure with Gated Graph Attention Networks for Sentence Matching0
Balanced Adversarial Training: Balancing Tradeoffs Between Oversensitivity and Undersensitivity in NLP Models0
Inter-Weighted Alignment Network for Sentence Pair Modeling0
AWE: Asymmetric Word Embedding for Textual Entailment0
SRIUBC: Simple Similarity Features for Semantic Textual Similarity0
String Re-writing Kernel0
Knowledge-Guided Paraphrase Identification0
LadRa-Net: Locally-Aware Dynamic Re-read Attention Net for Sentence Semantic Matching0
LCQMC:A Large-scale Chinese Question Matching Corpus0
Learning Context-Sensitive Convolutional Filters for Text Processing0
StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding0
Learning the Impact of Machine Translation Evaluation Metrics for Semantic Textual Similarity0
Learning to Recognize Ancillary Information for Automatic Paraphrase Identification0
STS-UHH at SemEval-2017 Task 1: Scoring Semantic Textual Similarity Using Supervised and Unsupervised Ensemble0
Learning to Selectively Transfer: Reinforced Transfer Learning for Deep Text Matching0
Leveraging Crowdsourcing for Paraphrase Recognition0
LIM-LIG at SemEval-2017 Task1: Enhancing the Semantic Similarity for Arabic Sentences with Vectors Weighting0
LIMSIILES: Basic English Substitution for Student Answer Assessment at SemEval 20130
Matching Natural Language Sentences with Hierarchical Sentence Factorization0
Matching Text with Deep Mutual Information Estimation0
Task-adaptive Pre-training and Self-training are Complementary for Natural Language Understanding0
Mining Social Science Publications for Survey Variables0
MITRE: Seven Systems for Semantic Similarity in Tweets0
Modelling Domain Relationships for Transfer Learning on Retrieval-based Question Answering Systems in E-commerce0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1BERT-BaseDirect Intrinsic Dimension9,295Unverified
2data2vecAccuracy92.4Unverified
3SMART-BERTDev Accuracy91.5Unverified
4ALICEF190.7Unverified
5MFAEAccuracy90.54Unverified
6RoBERTa-large 355M + Entailment as Few-shot LearnerF189.2Unverified
7MwAN Accuracy89.12Unverified
8DIINAccuracy89.06Unverified
9MSEMAccuracy88.86Unverified
10Bi-CAS-LSTMAccuracy88.6Unverified
#ModelMetricClaimedVerifiedStatus
1FEAT2, TFKLD, SVM, Fine-grained featuresAccuracy80.41Unverified
2NMF factorization-unigrams-TFKLDAccuracy72.75Unverified
3SWEM-concatAccuracy71.5Unverified
#ModelMetricClaimedVerifiedStatus
1BERT + SCH attmVal Accuracy91.42Unverified
2BERT + SCH attnVal F1 Score88.44Unverified
#ModelMetricClaimedVerifiedStatus
1CNN10 fold Cross validation50Unverified
#ModelMetricClaimedVerifiedStatus
1RoBETRa baseMCC0.53Unverified
#ModelMetricClaimedVerifiedStatus
1SplitEE-SAccuracy82.2Unverified
#ModelMetricClaimedVerifiedStatus
1TSDAEAP69.2Unverified
#ModelMetricClaimedVerifiedStatus
1Weighted Ensemble of TF-IDF and BERT Embeddings1:1 Accuracy82.04Unverified
#ModelMetricClaimedVerifiedStatus
1TSDAEAP76.8Unverified
#ModelMetricClaimedVerifiedStatus
1StructBERTRoBERTa ensembleAccuracy90.7Unverified
#ModelMetricClaimedVerifiedStatus
1SplitEE-SAccuracy76.7Unverified