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 51–100 of 172 papers

TitleStatusHype
Match-Prompt: Improving Multi-task Generalization Ability for Neural Text Matching via Prompt LearningCode0
Is Modularity Transferable? A Case Study through the Lens of Knowledge DistillationCode0
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
Learning to Represent Bilingual DictionariesCode0
Memory-efficient Stochastic methods for Memory-based TransformersCode0
Modelling Sentence Pairs with Tree-structured Attentive EncoderCode0
Multi-Task Deep Neural Networks for Natural Language UnderstandingCode0
Multiway Attention Networks for Modeling Sentence PairsCode0
Natural Language Inference over Interaction SpaceCode0
Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question AnsweringCode0
Paraphrase Thought: Sentence Embedding Module Imitating Human Language RecognitionCode0
PAWS: Paraphrase Adversaries from Word ScramblingCode0
Pay Attention when RequiredCode0
Recurrent Neural Network-Based Semantic Variational Autoencoder for Sequence-to-Sequence LearningCode0
RuPAWS: A Russian Adversarial Dataset for Paraphrase IdentificationCode0
Sentence Similarity Learning by Lexical Decomposition and CompositionCode0
Simple and Effective Text Matching with Richer Alignment FeaturesCode0
SpanBERT: Improving Pre-training by Representing and Predicting SpansCode0
SplitEE: Early Exit in Deep Neural Networks with Split ComputingCode0
To Attend or not to Attend: A Case Study on Syntactic Structures for Semantic RelatednessCode0
Tougher Text, Smarter Models: Raising the Bar for Adversarial Defence BenchmarksCode0
Towards Better Characterization of ParaphrasesCode0
Towards Better Characterization of ParaphrasesCode0
Training Complex Models with Multi-Task Weak SupervisionCode0
Transfer Fine-Tuning: A BERT Case StudyCode0
Inducing Alignment Structure with Gated Graph Attention Networks for Sentence Matching—0
Balanced Adversarial Training: Balancing Tradeoffs Between Oversensitivity and Undersensitivity in NLP Models—0
Inter-Weighted Alignment Network for Sentence Pair Modeling—0
AWE: Asymmetric Word Embedding for Textual Entailment—0
SRIUBC: Simple Similarity Features for Semantic Textual Similarity—0
String Re-writing Kernel—0
Knowledge-Guided Paraphrase Identification—0
LadRa-Net: Locally-Aware Dynamic Re-read Attention Net for Sentence Semantic Matching—0
LCQMC:A Large-scale Chinese Question Matching Corpus—0
Learning Context-Sensitive Convolutional Filters for Text Processing—0
StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding—0
Learning the Impact of Machine Translation Evaluation Metrics for Semantic Textual Similarity—0
Learning to Recognize Ancillary Information for Automatic Paraphrase Identification—0
STS-UHH at SemEval-2017 Task 1: Scoring Semantic Textual Similarity Using Supervised and Unsupervised Ensemble—0
Learning to Selectively Transfer: Reinforced Transfer Learning for Deep Text Matching—0
Leveraging Crowdsourcing for Paraphrase Recognition—0
LIM-LIG at SemEval-2017 Task1: Enhancing the Semantic Similarity for Arabic Sentences with Vectors Weighting—0
LIMSIILES: Basic English Substitution for Student Answer Assessment at SemEval 2013—0
Matching Natural Language Sentences with Hierarchical Sentence Factorization—0
Matching Text with Deep Mutual Information Estimation—0
Task-adaptive Pre-training and Self-training are Complementary for Natural Language Understanding—0
Mining Social Science Publications for Survey Variables—0
MITRE: Seven Systems for Semantic Similarity in Tweets—0
Modelling Domain Relationships for Transfer Learning on Retrieval-based Question Answering Systems in E-commerce—0
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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