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 101125 of 172 papers

TitleStatusHype
The BQ Corpus: A Large-scale Domain-specific Chinese Corpus For Sentence Semantic Equivalence Identification0
AWE: Asymmetric Word Embedding for Textual Entailment0
Cell-aware Stacked LSTMs for Modeling Sentences0
Paraphrase Thought: Sentence Embedding Module Imitating Human Language RecognitionCode0
Learning to Represent Bilingual DictionariesCode0
LCQMC:A Large-scale Chinese Question Matching Corpus0
Multiway Attention Networks for Modeling Sentence PairsCode0
To Attend or not to Attend: A Case Study on Syntactic Structures for Semantic RelatednessCode0
Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question AnsweringCode0
Element-wise Bilinear Interaction for Sentence Matching0
Semantic Sentence Matching with Densely-connected Recurrent and Co-attentive Information0
Character-based Neural Networks for Sentence Pair ModelingCode0
SPADE: Evaluation Dataset for Monolingual Phrase Alignment0
ETPC - A Paraphrase Identification Corpus Annotated with Extended Paraphrase Typology and NegationCode0
Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task LearningCode0
Matching Natural Language Sentences with Hierarchical Sentence Factorization0
Recurrent Neural Network-Based Semantic Variational Autoencoder for Sequence-to-Sequence LearningCode0
A Deep Relevance Matching Model for Ad-hoc RetrievalCode0
Modelling Domain Relationships for Transfer Learning on Retrieval-based Question Answering Systems in E-commerce0
Semantic Similarity Analysis for Paraphrase Identification in Arabic Texts0
Learning Context-Sensitive Convolutional Filters for Text Processing0
Natural Language Inference over Interaction SpaceCode0
Constructing narrative using a generative model and continuous action policies0
Inter-Weighted Alignment Network for Sentence Pair Modeling0
Mining Social Science Publications for Survey Variables0
Show:102550
← PrevPage 5 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