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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 101–150 of 172 papers

TitleStatusHype
Predicate-Argument Based Bi-Encoder for Paraphrase Identification—0
Contextualized Embeddings based Convolutional Neural Networks for Duplicate Question Identification—0
Reddit Temporal N-gram Corpus and its Applications on Paraphrase and Semantic Similarity in Social Media using a Topic-based Latent Semantic Analysis—0
Re-examining Machine Translation Metrics for Paraphrase Identification—0
Reference Scope Identification in Citing Sentences—0
An Optimal Quadratic Approach to Monolingual Paraphrase Alignment—0
Semantic Sentence Matching with Densely-connected Recurrent and Co-attentive Information—0
Semantic Similarity Analysis for Paraphrase Identification in Arabic Texts—0
SemEval-2013 Task 5: Evaluating Phrasal Semantics—0
SemEval-2015 Task 1: Paraphrase and Semantic Similarity in Twitter (PIT)—0
SEMILAR: The Semantic Similarity Toolkit—0
Semi-Markov Phrase-Based Monolingual Alignment—0
Sentence Alignment using Unfolding Recursive Autoencoders—0
Co-Stack Residual Affinity Networks with Multi-level Attention Refinement for Matching Text Sequences—0
Accurate, yet inconsistent? Consistency Analysis on Language Understanding Models—0
SMoA: Sparse Mixture of Adapters to Mitigate Multiple Dataset Biases—0
SPADE: Evaluation Dataset for Monolingual Phrase Alignment—0
Cross-Lingual Adaptation Using Universal Dependencies—0
Cross-lingual paraphrase identification—0
SRIUBC: Simple Similarity Features for Semantic Textual Similarity—0
String Re-writing Kernel—0
Multiway Attention Networks for Modeling Sentence PairsCode0
Sentence Embeddings for Russian NLUCode0
ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence PairsCode0
Adaptation of Deep Bidirectional Multilingual Transformers for Russian LanguageCode0
A Deep Relevance Matching Model for Ad-hoc RetrievalCode0
Adversarial Self-Attention for Language UnderstandingCode0
Application Specific Compression of Deep Learning ModelsCode0
Assessing Word Importance Using Models Trained for Semantic TasksCode0
A Study of MatchPyramid Models on Ad-hoc RetrievalCode0
Balanced Adversarial Training: Balancing Tradeoffs between Fickleness and Obstinacy in NLP ModelsCode0
Bilateral Multi-Perspective Matching for Natural Language SentencesCode0
Building a Non-Trivial Paraphrase Corpus Using Multiple Machine Translation SystemsCode0
Character-based Neural Networks for Sentence Pair ModelingCode0
Co-Driven Recognition of Semantic Consistency via the Fusion of Transformer and HowNet Sememes KnowledgeCode0
Convolutional Neural Network for Paraphrase IdentificationCode0
Cross-functional Analysis of Generalisation in Behavioural LearningCode0
Dice Loss for Data-imbalanced NLP TasksCode0
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
ERNIE: Enhanced Language Representation with Informative EntitiesCode0
ETPC - A Paraphrase Identification Corpus Annotated with Extended Paraphrase Typology and NegationCode0
Evaluating Multilingual Sentence Representation Models in a Real Case ScenarioCode0
Explaining Neural Network Predictions on Sentence Pairs via Learning Word-Group MasksCode0
GAPX: Generalized Autoregressive Paraphrase-Identification XCode0
Idiom Paraphrases: Seventh Heaven vs Cloud NineCode0
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
Show:102550
← PrevPage 3 of 4Next →
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