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

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Showing 1–50 of 172 papers

TitleStatusHype
Evaluating the Effectiveness of Linguistic Knowledge in Pretrained Language Models: A Case Study of Universal Dependencies—0
Enhancing Paraphrase Type Generation: The Impact of DPO and RLHF Evaluated with Human-Ranked DataCode0
Enhancing Plagiarism Detection in Marathi with a Weighted Ensemble of TF-IDF and BERT Embeddings for Low-Resource Language ProcessingCode0
Tougher Text, Smarter Models: Raising the Bar for Adversarial Defence BenchmarksCode0
Application Specific Compression of Deep Learning ModelsCode0
Cross-lingual paraphrase identification—0
Is Modularity Transferable? A Case Study through the Lens of Knowledge DistillationCode0
Memory-efficient Stochastic methods for Memory-based TransformersCode0
SplitEE: Early Exit in Deep Neural Networks with Split ComputingCode0
Do Multilingual Language Models Think Better in English?Code1
Is Prompt-Based Finetuning Always Better than Vanilla Finetuning? Insights from Cross-Lingual Language UnderstandingCode0
Assessing Word Importance Using Models Trained for Semantic TasksCode0
Cross-functional Analysis of Generalisation in Behavioural LearningCode0
SMoA: Sparse Mixture of Adapters to Mitigate Multiple Dataset Biases—0
Co-Driven Recognition of Semantic Consistency via the Fusion of Transformer and HowNet Sememes KnowledgeCode0
Paraphrase Identification with Deep Learning: A Review of Datasets and Methods—0
Improving word mover's distance by leveraging self-attention matrixCode1
Scaling Instruction-Finetuned Language ModelsCode3
Balanced Adversarial Training: Balancing Tradeoffs between Fickleness and Obstinacy in NLP ModelsCode0
Towards Structure-aware Paraphrase Identification with Phrase Alignment Using Sentence Encoders—0
Improving Large-scale Paraphrase Acquisition and Generation—0
GAPX: Generalized Autoregressive Paraphrase-Identification XCode0
Paraphrasing, textual entailment, and semantic similarity above word level—0
Towards Automatic Short Answer Assessment for Finnish as a Paraphrase Retrieval Task—0
Adversarial Self-Attention for Language UnderstandingCode0
RuPAWS: A Russian Adversarial Dataset for Paraphrase IdentificationCode0
Evaluating Multilingual Sentence Representation Models in a Real Case ScenarioCode0
Predicate-Argument Based Bi-Encoder for Paraphrase Identification—0
Towards Better Characterization of ParaphrasesCode0
NMTScore: A Multilingual Analysis of Translation-based Text Similarity MeasuresCode1
Match-Prompt: Improving Multi-task Generalization Ability for Neural Text Matching via Prompt LearningCode0
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and LanguageCode1
PerPaDa: A Persian Paraphrase Dataset based on Implicit Crowdsourcing Data Collection—0
Balanced Adversarial Training: Balancing Tradeoffs Between Oversensitivity and Undersensitivity in NLP Models—0
Explaining Predictive Uncertainty by Looking Back at Model Explanations—0
BnPC: A Corpus for Paraphrase Detection in Bangla—0
Combining Shallow and Deep Representations for Text-Pair Classification—0
Predicate-Argument Based Bi-Encoder for Paraphrase Identification—0
Knowledge-Guided Paraphrase Identification—0
Trans-Encoder: Unsupervised sentence-pair modelling through self- and mutual-distillationsCode1
Towards Better Characterization of ParaphrasesCode0
Task-adaptive Pre-training and Self-training are Complementary for Natural Language Understanding—0
How much pretraining data do language models need to learn syntax?—0
Contextualized Embeddings based Convolutional Neural Networks for Duplicate Question Identification—0
Does BERT Understand Idioms? A Probing-Based Empirical Study of BERT Encodings of Idioms—0
Towards Domain-Generalizable Paraphrase Identification by Avoiding the Shortcut Learning—0
Assessing the Eligibility of Backtranslated Samples Based on Semantic Similarity for the Paraphrase Identification Task—0
Accurate, yet inconsistent? Consistency Analysis on Language Understanding Models—0
LadRa-Net: Locally-Aware Dynamic Re-read Attention Net for Sentence Semantic Matching—0
Modelling Latent Translations for Cross-Lingual TransferCode1
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All datasetsQuora Question PairsMSRPQuora Question Pairs Dev2017_test setAPIMDbPITTranslated SNLI Dataset in MarathiTURLWikiHopYelp

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