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
Experiments on Paraphrase Identification Using Quora Question Pairs Dataset—0
AWE: Asymmetric Word Embedding for Textual Entailment—0
Explaining Predictive Uncertainty by Looking Back at Model Explanations—0
Exploiting Sentence Similarities for Better Alignments—0
FBK-HLT: An Effective System for Paraphrase Identification and Semantic Similarity in Twitter—0
FBK HLT-MT at SemEval-2016 Task 1: Cross-lingual Semantic Similarity Measurement Using Quality Estimation Features and Compositional Bilingual Word Embeddings—0
Balanced Adversarial Training: Balancing Tradeoffs Between Oversensitivity and Undersensitivity in NLP Models—0
HLTC-HKUST: A Neural Network Paraphrase Classifier using Translation Metrics, Semantic Roles and Lexical Similarity Features—0
How much pretraining data do language models need to learn syntax?—0
A Cross-Sentence Latent Variable Model for Semi-Supervised Text Sequence Matching—0
Improving Large-scale Paraphrase Acquisition and Generation—0
Inducing Alignment Structure with Gated Graph Attention Networks for Sentence Matching—0
Inter-Weighted Alignment Network for Sentence Pair Modeling—0
Better Early than Late: Fusing Topics with Word Embeddings for Neural Question Paraphrase Identification—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
Learning the Impact of Machine Translation Evaluation Metrics for Semantic Textual Similarity—0
Learning to Recognize Ancillary Information for Automatic Paraphrase Identification—0
BnPC: A Corpus for Paraphrase Detection in Bangla—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
Bridging the Gap between Relevance Matching and Semantic Matching for Short Text Similarity Modeling—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
AMRITA\_CEN@SemEval-2015: Paraphrase Detection for Twitter using Unsupervised Feature Learning with Recursive Autoencoders—0
MultiGranCNN: An Architecture for General Matching of Text Chunks on Multiple Levels of Granularity—0
Multi-Perspective Sentence Similarity Modeling with Convolutional Neural Networks—0
Multiresolution Graph Attention Networks for Relevance Matching—0
Content Selection through Paraphrase Detection: Capturing different Semantic Realisations of the Same Idea—0
Multi-task Sentence Encoding Model for Semantic Retrieval in Question Answering Systems—0
An Exploration of Embeddings for Generalized Phrases—0
Neural Paraphrase Identification of Questions with Noisy Pretraining—0
On Paraphrase Identification Corpora—0
Original Semantics-Oriented Attention and Deep Fusion Network for Sentence Matching—0
Paraphrase Identification and Semantic Similarity in Twitter with Simple Features—0
Paraphrase Identification with Deep Learning: A Review of Datasets and Methods—0
Combining Shallow and Deep Representations for Text-Pair Classification—0
Paraphrasing, textual entailment, and semantic similarity above word level—0
Constructing narrative using a generative model and continuous action policies—0
Cell-aware Stacked LSTMs for Modeling Sentences—0
PerPaDa: A Persian Paraphrase Dataset based on Implicit Crowdsourcing Data Collection—0
Pointwise Paraphrase Appraisal is Potentially Problematic—0
Predicate-Argument Based Bi-Encoder for Paraphrase Identification—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