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 1–25 of 172 papers

TitleStatusHype
BERT: Pre-training of Deep Bidirectional Transformers for Language UnderstandingCode3
Scaling Instruction-Finetuned Language ModelsCode3
PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase IdentificationCode2
What Do Questions Exactly Ask? MFAE: Duplicate Question Identification with Multi-Fusion Asking EmphasisCode1
Self-Explaining Structures Improve NLP ModelsCode1
TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding LearningCode1
NMTScore: A Multilingual Analysis of Translation-based Text Similarity MeasuresCode1
Factorising Meaning and Form for Intent-Preserving ParaphrasingCode1
RealFormer: Transformer Likes Residual AttentionCode1
Modelling Latent Translations for Cross-Lingual TransferCode1
Charformer: Fast Character Transformers via Gradient-based Subword TokenizationCode1
An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language ModelsCode1
SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized OptimizationCode1
Trans-Encoder: Unsupervised sentence-pair modelling through self- and mutual-distillationsCode1
BET: A Backtranslation Approach for Easy Data Augmentation in Transformer-based Paraphrase Identification ContextCode1
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and LanguageCode1
Do Multilingual Language Models Think Better in English?Code1
Entailment as Few-Shot LearnerCode1
Improving Paraphrase Detection with the Adversarial Paraphrasing TaskCode1
Improving word mover's distance by leveraging self-attention matrixCode1
Adversarial Semantic CollisionsCode1
Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-TuningCode1
FNet: Mixing Tokens with Fourier TransformsCode1
PARADE: A New Dataset for Paraphrase Identification Requiring Computer Science Domain KnowledgeCode1
XLNet: Generalized Autoregressive Pretraining for Language UnderstandingCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1BERT-BaseDirect Intrinsic Dimension9,295—Unverified
2data2vecAccuracy92.4—Unverified
3SMART-BERTDev Accuracy91.5—Unverified
4ALICEF190.7—Unverified
5MFAEAccuracy90.54—Unverified
6RoBERTa-large 355M + Entailment as Few-shot LearnerF189.2—Unverified
7MwAN Accuracy89.12—Unverified
8DIINAccuracy89.06—Unverified
9MSEMAccuracy88.86—Unverified
10Bi-CAS-LSTMAccuracy88.6—Unverified
#ModelMetricClaimedVerifiedStatus
1FEAT2, TFKLD, SVM, Fine-grained featuresAccuracy80.41—Unverified
2NMF factorization-unigrams-TFKLDAccuracy72.75—Unverified
3SWEM-concatAccuracy71.5—Unverified
#ModelMetricClaimedVerifiedStatus
1BERT + SCH attmVal Accuracy91.42—Unverified
2BERT + SCH attnVal F1 Score88.44—Unverified
#ModelMetricClaimedVerifiedStatus
1CNN10 fold Cross validation50—Unverified
#ModelMetricClaimedVerifiedStatus
1RoBETRa baseMCC0.53—Unverified
#ModelMetricClaimedVerifiedStatus
1SplitEE-SAccuracy82.2—Unverified
#ModelMetricClaimedVerifiedStatus
1TSDAEAP69.2—Unverified
#ModelMetricClaimedVerifiedStatus
1Weighted Ensemble of TF-IDF and BERT Embeddings1:1 Accuracy82.04—Unverified
#ModelMetricClaimedVerifiedStatus
1TSDAEAP76.8—Unverified
#ModelMetricClaimedVerifiedStatus
1StructBERTRoBERTa ensembleAccuracy90.7—Unverified
#ModelMetricClaimedVerifiedStatus
1SplitEE-SAccuracy76.7—Unverified