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
Towards Better Characterization of ParaphrasesCode0
Match-Prompt: Improving Multi-task Generalization Ability for Neural Text Matching via Prompt LearningCode0
PerPaDa: A Persian Paraphrase Dataset based on Implicit Crowdsourcing Data Collection—0
Balanced Adversarial Training: Balancing Tradeoffs Between Oversensitivity and Undersensitivity in NLP Models—0
Explaining Predictive Uncertainty by Looking Back at Model Explanations—0
BnPC: A Corpus for Paraphrase Detection in Bangla—0
Combining Shallow and Deep Representations for Text-Pair Classification—0
Predicate-Argument Based Bi-Encoder for Paraphrase Identification—0
Knowledge-Guided Paraphrase Identification—0
Towards Better Characterization of ParaphrasesCode0
Task-adaptive Pre-training and Self-training are Complementary for Natural Language Understanding—0
How much pretraining data do language models need to learn syntax?—0
Contextualized Embeddings based Convolutional Neural Networks for Duplicate Question Identification—0
Assessing the Eligibility of Backtranslated Samples Based on Semantic Similarity for the Paraphrase Identification Task—0
Does BERT Understand Idioms? A Probing-Based Empirical Study of BERT Encodings of Idioms—0
Towards Domain-Generalizable Paraphrase Identification by Avoiding the Shortcut Learning—0
Accurate, yet inconsistent? Consistency Analysis on Language Understanding Models—0
LadRa-Net: Locally-Aware Dynamic Re-read Attention Net for Sentence Semantic Matching—0
Explaining Neural Network Predictions on Sentence Pairs via Learning Word-Group MasksCode0
XLA: A Robust Unsupervised Data Augmentation Framework for Cross-Lingual NLP—0
Inducing Alignment Structure with Gated Graph Attention Networks for Sentence Matching—0
Pay Attention when RequiredCode0
Better Early than Late: Fusing Topics with Word Embeddings for Neural Question Paraphrase Identification—0
Experiments on Paraphrase Identification Using Quora Question Pairs Dataset—0
Pointwise Paraphrase Appraisal is Potentially Problematic—0
Cross-Lingual Adaptation Using Universal Dependencies—0
TRANS-BLSTM: Transformer with Bidirectional LSTM for Language Understanding—0
Matching Text with Deep Mutual Information Estimation—0
Multi-task Sentence Encoding Model for Semantic Retrieval in Question Answering Systems—0
Dice Loss for Data-imbalanced NLP TasksCode0
Bridging the Gap between Relevance Matching and Semantic Matching for Short Text Similarity Modeling—0
Original Semantics-Oriented Attention and Deep Fusion Network for Sentence Matching—0
Sentence Embeddings for Russian NLUCode0
TinyBERT: Distilling BERT for Natural Language UnderstandingCode0
Robustness to Modification with Shared Words in Paraphrase Identification—0
Transfer Fine-Tuning: A BERT Case StudyCode0
A Qualitative Evaluation Framework for Paraphrase Identification—0
StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding—0
Simple and Effective Text Matching with Richer Alignment FeaturesCode0
SpanBERT: Improving Pre-training by Representing and Predicting SpansCode0
A Cross-Sentence Latent Variable Model for Semi-Supervised Text Sequence Matching—0
Unsupervised Paraphrasing without Translation—0
Adaptation of Deep Bidirectional Multilingual Transformers for Russian LanguageCode0
ERNIE: Enhanced Language Representation with Informative EntitiesCode0
PAWS: Paraphrase Adversaries from Word ScramblingCode0
Multiresolution Graph Attention Networks for Relevance Matching—0
Multi-Task Deep Neural Networks for Natural Language UnderstandingCode0
Learning to Selectively Transfer: Reinforced Transfer Learning for Deep Text Matching—0
Co-Stack Residual Affinity Networks with Multi-level Attention Refinement for Matching Text Sequences—0
Training Complex Models with Multi-Task Weak SupervisionCode0
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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