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Found 8 papers, 7 papers with code
Black-Box Tuning for Language-Model-as-a-Service
In such a scenario, which we call Language-Model-as-a-Service (LMaaS), the gradients of PTMs are usually unavailable.
CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation
In this paper, we take the advantage of previous pre-trained models (PTMs) and propose a novel Chinese Pre-trained Unbalanced Transformer (CPT).
Accelerating BERT Inference for Sequence Labeling via Early-Exit
To alleviate this problem, we extend the recent successful early-exit mechanism to accelerate the inference of PTMs for sequence labeling tasks.
Generating Adversarial Examples in Chinese Texts Using Sentence-Pieces
The substitutions in the generated adversarial examples are not characters or words but \textit{'pieces'}, which are more natural to Chinese readers.
CoLAKE: Contextualized Language and Knowledge Embedding
With the emerging branch of incorporating factual knowledge into pre-trained language models such as BERT, most existing models consider shallow, static, and separately pre-trained entity embeddings, which limits the performance gains of these models.
Pre-trained Models for Natural Language Processing: A Survey
Recently, the emergence of pre-trained models (PTMs) has brought natural language processing (NLP) to a new era.
Learning Sparse Sharing Architectures for Multiple Tasks
Most existing deep multi-task learning models are based on parameter sharing, such as hard sharing, hierarchical sharing, and soft sharing.
Star-Transformer
Although Transformer has achieved great successes on many NLP tasks, its heavy structure with fully-connected attention connections leads to dependencies on large training data.
Ranked #12 on
Sentiment Analysis
on SST-5 Fine-grained classification
Named Entity Recognition (NER)
Natural Language Inference
+2
