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 150 of 172 papers

TitleStatusHype
Scaling Instruction-Finetuned Language ModelsCode3
BERT: Pre-training of Deep Bidirectional Transformers for Language UnderstandingCode3
PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase IdentificationCode2
BET: A Backtranslation Approach for Easy Data Augmentation in Transformer-based Paraphrase Identification ContextCode1
Charformer: Fast Character Transformers via Gradient-based Subword TokenizationCode1
RealFormer: Transformer Likes Residual AttentionCode1
TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding LearningCode1
XLNet: Generalized Autoregressive Pretraining for Language UnderstandingCode1
Improving Paraphrase Detection with the Adversarial Paraphrasing TaskCode1
FNet: Mixing Tokens with Fourier TransformsCode1
SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized OptimizationCode1
An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language ModelsCode1
Modelling Latent Translations for Cross-Lingual TransferCode1
PARADE: A New Dataset for Paraphrase Identification Requiring Computer Science Domain KnowledgeCode1
Do Multilingual Language Models Think Better in English?Code1
NMTScore: A Multilingual Analysis of Translation-based Text Similarity MeasuresCode1
Trans-Encoder: Unsupervised sentence-pair modelling through self- and mutual-distillationsCode1
What Do Questions Exactly Ask? MFAE: Duplicate Question Identification with Multi-Fusion Asking EmphasisCode1
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and LanguageCode1
Adversarial Semantic CollisionsCode1
Factorising Meaning and Form for Intent-Preserving ParaphrasingCode1
Improving word mover's distance by leveraging self-attention matrixCode1
Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-TuningCode1
Self-Explaining Structures Improve NLP ModelsCode1
Entailment as Few-Shot LearnerCode1
Modelling Sentence Pairs with Tree-structured Attentive EncoderCode0
Bilateral Multi-Perspective Matching for Natural Language SentencesCode0
Adaptation of Deep Bidirectional Multilingual Transformers for Russian LanguageCode0
Memory-efficient Stochastic methods for Memory-based TransformersCode0
Learning to Represent Bilingual DictionariesCode0
Multi-Task Deep Neural Networks for Natural Language UnderstandingCode0
Is Modularity Transferable? A Case Study through the Lens of Knowledge DistillationCode0
Dice Loss for Data-imbalanced NLP TasksCode0
Is Prompt-Based Finetuning Always Better than Vanilla Finetuning? Insights from Cross-Lingual Language UnderstandingCode0
Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task LearningCode0
Multiway Attention Networks for Modeling Sentence PairsCode0
Match-Prompt: Improving Multi-task Generalization Ability for Neural Text Matching via Prompt LearningCode0
GAPX: Generalized Autoregressive Paraphrase-Identification XCode0
Idiom Paraphrases: Seventh Heaven vs Cloud NineCode0
Cross-functional Analysis of Generalisation in Behavioural LearningCode0
A Study of MatchPyramid Models on Ad-hoc RetrievalCode0
Convolutional Neural Network for Paraphrase IdentificationCode0
Assessing Word Importance Using Models Trained for Semantic TasksCode0
Adversarial Self-Attention for Language UnderstandingCode0
ERNIE: Enhanced Language Representation with Informative EntitiesCode0
TinyBERT: Distilling BERT for Natural Language UnderstandingCode0
ETPC - A Paraphrase Identification Corpus Annotated with Extended Paraphrase Typology and NegationCode0
ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence PairsCode0
Balanced Adversarial Training: Balancing Tradeoffs between Fickleness and Obstinacy in NLP ModelsCode0
Sentence Embeddings for Russian NLUCode0
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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