SOTAVerified

Sentence-Pair Classification

Papers

Showing 125 of 38 papers

TitleStatusHype
On the effectiveness of Large Language Models in the mechanical design domainCode0
Can linguists better understand DNA?Code0
Generating Synthetic Datasets for Few-shot Prompt Tuning0
New Datasets for Automatic Detection of Textual Entailment and of Contradictions between Sentences in FrenchCode0
A Generic NLI approach for Classification of Sentiment Associated with Therapies0
Link Prediction for Wikipedia Articles as a Natural Language Inference TaskCode0
DACCORD : un jeu de données pour la Détection Automatique d'énonCés COntRaDictoires en françaisCode0
mPMR: A Multilingual Pre-trained Machine Reader at ScaleCode0
Data Augmentation for Conflict and Duplicate Detection in Software Engineering Sentence Pairs0
Transfer learning for conflict and duplicate detection in software requirement pairsCode0
Fine-mixing: Mitigating Backdoors in Fine-tuned Language ModelsCode8
YNU-HPCC at SemEval-2022 Task 6: Transformer-based Model for Intended Sarcasm Detection in English and Arabic0
Constructing A Dataset of Support and Attack Relations in Legal Arguments in Court Judgements using Linguistic Rules0
Multi-label topic classification for COVID-19 literature with Bioformer0
CBLUE: A Chinese Biomedical Language Understanding EvaluationBenchmark0
Sparse Distillation: Speeding Up Text Classification by Using Bigger Student ModelsCode1
Revisiting Self-Training for Few-Shot Learning of Language ModelCode1
Enhancing Biomedical Relation Extraction with Transformer Models using Shortest Dependency Path Features and Triplet InformationCode0
Avoiding Inference Heuristics in Few-shot Prompt-based FinetuningCode1
Unsupervised Pre-training with Structured Knowledge for Improving Natural Language Inference0
FewCLUE: A Chinese Few-shot Learning Evaluation BenchmarkCode1
CBLUE: A Chinese Biomedical Language Understanding Evaluation BenchmarkCode1
Neural semi-Markov CRF for Monolingual Word AlignmentCode1
MCL@IITK at SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation using Augmented Data, Signals, and Transformers0
Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP ModelsCode1
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