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Sentence-Pair Classification

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Showing 1–25 of 38 papers

TitleStatusHype
Fine-mixing: Mitigating Backdoors in Fine-tuned Language ModelsCode8
CLUE: A Chinese Language Understanding Evaluation BenchmarkCode2
Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP ModelsCode1
Revisiting Self-Training for Few-Shot Learning of Language ModelCode1
FewCLUE: A Chinese Few-shot Learning Evaluation BenchmarkCode1
CBLUE: A Chinese Biomedical Language Understanding Evaluation BenchmarkCode1
Sparse Distillation: Speeding Up Text Classification by Using Bigger Student ModelsCode1
Neural semi-Markov CRF for Monolingual Word AlignmentCode1
Avoiding Inference Heuristics in Few-shot Prompt-based FinetuningCode1
Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training ApproachCode1
YNU-HPCC at SemEval-2022 Task 6: Transformer-based Model for Intended Sarcasm Detection in English and Arabic—0
A Generic NLI approach for Classification of Sentiment Associated with Therapies—0
CBLUE: A Chinese Biomedical Language Understanding EvaluationBenchmark—0
Constructing A Dataset of Support and Attack Relations in Legal Arguments in Court Judgements using Linguistic Rules—0
Data Augmentation for Conflict and Duplicate Detection in Software Engineering Sentence Pairs—0
End-to-End Resume Parsing and Finding Candidates for a Job Description using BERT—0
Generating Synthetic Datasets for Few-shot Prompt Tuning—0
Generating Token-Level Explanations for Natural Language Inference—0
MCL@IITK at SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation using Augmented Data, Signals, and Transformers—0
Multi-label topic classification for COVID-19 literature with Bioformer—0
Sentence Encoding with Tree-constrained Relation Networks—0
Transformers on Sarcasm Detection with Context—0
Understanding Advertisements with BERT—0
Unsupervised Pre-training with Structured Knowledge for Improving Natural Language Inference—0
Why and How to Pay Different Attention to Phrase Alignments of Different Intensities—0
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