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 76100 of 172 papers

TitleStatusHype
Matching Natural Language Sentences with Hierarchical Sentence Factorization0
Matching Text with Deep Mutual Information Estimation0
Bridging the Gap between Relevance Matching and Semantic Matching for Short Text Similarity Modeling0
Mining Social Science Publications for Survey Variables0
MITRE: Seven Systems for Semantic Similarity in Tweets0
Modelling Domain Relationships for Transfer Learning on Retrieval-based Question Answering Systems in E-commerce0
AMRITA\_CEN@SemEval-2015: Paraphrase Detection for Twitter using Unsupervised Feature Learning with Recursive Autoencoders0
MultiGranCNN: An Architecture for General Matching of Text Chunks on Multiple Levels of Granularity0
Multi-Perspective Sentence Similarity Modeling with Convolutional Neural Networks0
Multiresolution Graph Attention Networks for Relevance Matching0
Content Selection through Paraphrase Detection: Capturing different Semantic Realisations of the Same Idea0
Multi-task Sentence Encoding Model for Semantic Retrieval in Question Answering Systems0
An Exploration of Embeddings for Generalized Phrases0
Neural Paraphrase Identification of Questions with Noisy Pretraining0
On Paraphrase Identification Corpora0
Original Semantics-Oriented Attention and Deep Fusion Network for Sentence Matching0
Paraphrase Identification and Semantic Similarity in Twitter with Simple Features0
Paraphrase Identification with Deep Learning: A Review of Datasets and Methods0
Combining Shallow and Deep Representations for Text-Pair Classification0
Paraphrasing, textual entailment, and semantic similarity above word level0
Constructing narrative using a generative model and continuous action policies0
Cell-aware Stacked LSTMs for Modeling Sentences0
PerPaDa: A Persian Paraphrase Dataset based on Implicit Crowdsourcing Data Collection0
Pointwise Paraphrase Appraisal is Potentially Problematic0
Predicate-Argument Based Bi-Encoder for Paraphrase Identification0
Show:102550
← PrevPage 4 of 7Next →

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