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 101–150 of 172 papers

TitleStatusHype
The BQ Corpus: A Large-scale Domain-specific Chinese Corpus For Sentence Semantic Equivalence Identification—0
AWE: Asymmetric Word Embedding for Textual Entailment—0
Cell-aware Stacked LSTMs for Modeling Sentences—0
Paraphrase Thought: Sentence Embedding Module Imitating Human Language RecognitionCode0
Learning to Represent Bilingual DictionariesCode0
LCQMC:A Large-scale Chinese Question Matching Corpus—0
To Attend or not to Attend: A Case Study on Syntactic Structures for Semantic RelatednessCode0
Multiway Attention Networks for Modeling Sentence PairsCode0
Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question AnsweringCode0
Element-wise Bilinear Interaction for Sentence Matching—0
Semantic Sentence Matching with Densely-connected Recurrent and Co-attentive Information—0
Character-based Neural Networks for Sentence Pair ModelingCode0
ETPC - A Paraphrase Identification Corpus Annotated with Extended Paraphrase Typology and NegationCode0
SPADE: Evaluation Dataset for Monolingual Phrase Alignment—0
Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task LearningCode0
Matching Natural Language Sentences with Hierarchical Sentence Factorization—0
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-commerce—0
Semantic Similarity Analysis for Paraphrase Identification in Arabic Texts—0
Learning Context-Sensitive Convolutional Filters for Text Processing—0
Natural Language Inference over Interaction SpaceCode0
Inter-Weighted Alignment Network for Sentence Pair Modeling—0
Constructing narrative using a generative model and continuous action policies—0
LIM-LIG at SemEval-2017 Task1: Enhancing the Semantic Similarity for Arabic Sentences with Vectors Weighting—0
A Continuously Growing Dataset of Sentential Paraphrases—0
STS-UHH at SemEval-2017 Task 1: Scoring Semantic Textual Similarity Using Supervised and Unsupervised Ensemble—0
Mining Social Science Publications for Survey Variables—0
Sentence Alignment using Unfolding Recursive Autoencoders—0
Building a Non-Trivial Paraphrase Corpus Using Multiple Machine Translation SystemsCode0
Neural Paraphrase Identification of Questions with Noisy Pretraining—0
Bilateral Multi-Perspective Matching for Natural Language SentencesCode0
Deep Learning of Binary and Gradient Judgements for Semantic Paraphrase—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
Exploiting Sentence Similarities for Better Alignments—0
Modelling Sentence Pairs with Tree-structured Attentive EncoderCode0
Content Selection through Paraphrase Detection: Capturing different Semantic Realisations of the Same Idea—0
A Study of MatchPyramid Models on Ad-hoc RetrievalCode0
UTA DLNLP at SemEval-2016 Task 12: Deep Learning Based Natural Language Processing System for Clinical Information Identification from Clinical Notes and Pathology Reports—0
Learning to Recognize Ancillary Information for Automatic Paraphrase Identification—0
ASOBEK at SemEval-2016 Task 1: Sentence Representation with Character N-gram Embeddings for Semantic Textual Similarity—0
FBK HLT-MT at SemEval-2016 Task 1: Cross-lingual Semantic Similarity Measurement Using Quality Estimation Features and Compositional Bilingual Word Embeddings—0
Discriminative Phrase Embedding for Paraphrase Identification—0
Sentence Similarity Learning by Lexical Decomposition and CompositionCode0
ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence PairsCode0
Learning the Impact of Machine Translation Evaluation Metrics for Semantic Textual Similarity—0
Multi-Perspective Sentence Similarity Modeling with Convolutional Neural Networks—0
Idiom Paraphrases: Seventh Heaven vs Cloud NineCode0
A Unified Kernel Approach for Learning Typed Sentence Rewritings—0
MultiGranCNN: An Architecture for General Matching of Text Chunks on Multiple Levels of Granularity—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