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
A Unified Kernel Approach for Learning Typed Sentence Rewritings—0
The BQ Corpus: A Large-scale Domain-specific Chinese Corpus For Sentence Semantic Equivalence Identification—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
UMCC\_DLSI-(EPS): Paraphrases Detection Based on Semantic Distance—0
Multi-task Sentence Encoding Model for Semantic Retrieval in Question Answering Systems—0
Unsupervised Paraphrasing without Translation—0
Towards Automatic Short Answer Assessment for Finnish as a Paraphrase Retrieval Task—0
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
Neural Paraphrase Identification of Questions with Noisy Pretraining—0
Assessing the Eligibility of Backtranslated Samples Based on Semantic Similarity for the Paraphrase Identification Task—0
On Paraphrase Identification Corpora—0
Original Semantics-Oriented Attention and Deep Fusion Network for Sentence Matching—0
ASOBEK at SemEval-2016 Task 1: Sentence Representation with Character N-gram Embeddings for Semantic Textual Similarity—0
Paraphrase Identification and Semantic Similarity in Twitter with Simple Features—0
Paraphrase Identification with Deep Learning: A Review of Datasets and Methods—0
A Continuously Growing Dataset of Sentential Paraphrases—0
Paraphrasing, textual entailment, and semantic similarity above word level—0
Towards Domain-Generalizable Paraphrase Identification by Avoiding the Shortcut Learning—0
A Qualitative Evaluation Framework for Paraphrase Identification—0
Towards Structure-aware Paraphrase Identification with Phrase Alignment Using Sentence Encoders—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
Predicate-Argument Based Bi-Encoder for Paraphrase Identification—0
XLA: A Robust Unsupervised Data Augmentation Framework for Cross-Lingual NLP—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
Re-examining Machine Translation Metrics for Paraphrase Identification—0
Reference Scope Identification in Citing Sentences—0
TRANS-BLSTM: Transformer with Bidirectional LSTM for Language Understanding—0
An Optimal Quadratic Approach to Monolingual Paraphrase Alignment—0
An Exploration of Embeddings for Generalized Phrases—0
Semantic Sentence Matching with Densely-connected Recurrent and Co-attentive Information—0
Semantic Similarity Analysis for Paraphrase Identification in Arabic Texts—0
Contextualized Embeddings based Convolutional Neural Networks for Duplicate Question Identification—0
SemEval-2013 Task 5: Evaluating Phrasal Semantics—0
Co-Stack Residual Affinity Networks with Multi-level Attention Refinement for Matching Text Sequences—0
SemEval-2015 Task 1: Paraphrase and Semantic Similarity in Twitter (PIT)—0
Cross-Lingual Adaptation Using Universal Dependencies—0
Cross-lingual paraphrase identification—0
Content Selection through Paraphrase Detection: Capturing different Semantic Realisations of the Same Idea—0
Deep Learning of Binary and Gradient Judgements for Semantic Paraphrase—0
SEMILAR: The Semantic Similarity Toolkit—0
Discriminative Improvements to Distributional Sentence Similarity—0
Discriminative Phrase Embedding for Paraphrase Identification—0
Does BERT Understand Idioms? A Probing-Based Empirical Study of BERT Encodings of Idioms—0
Constructing narrative using a generative model and continuous action policies—0
Element-wise Bilinear Interaction for Sentence Matching—0
Semi-Markov Phrase-Based Monolingual Alignment—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